Name: Python for Deep Learning - Build Neural Networks in Python
Price: 37.99 GBP
Availability: InStock

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.</strong>
        <p>TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning.

In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization.

By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning.</p>
        <h2>What You Will Learn</h2><p>Understand what machine learning is<br>Build linear models with TensorFlow 2<br>Learn how to build deep neural networks with TensorFlow 2<br>Learn how to perform image classification and regression with ANN<br>Learn loss functions such as mean-squared error and cross-entropy loss<br>Learn about stochastic gradient descent, momentum, and Adam optimization</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers.<br><br>One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build artificial neural networks. The course is well-balanced with theory that explains the ANN concepts and hands-on coding exercises for practical understanding.</p>
        <h2>Key Features</h2><p>Understand the utilization of TensorFlow 2 to construct artificial neural networks * The course covers the basics of machine learning, classification, and regression * Explore the connection between artificial neural networks and biological neural networks</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Machine Learning and Neurons</h4><p>In this section, we will cover a crash course on machine learning and neurons. You will learn about classification and regression, and their TensorFlow implementations.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Machine Learning?</p><p style='margin: 0'>In this video, we will understand what machine learning is and get a clear idea about ML (machine learning).</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (Classification Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear classification for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification Notebook</p><p style='margin: 0'>In this video, we will understand linear classification with the help of breast cancer dataset example.</p></td></tr><tr><td style='padding: 15px;'><p>4. Code Preparation (Regression Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear regression for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression Notebook</p><p style='margin: 0'>In this video, we will understand linear regression by proving the Mosse law true.</p></td></tr><tr><td style='padding: 15px;'><p>6. The Neuron</p><p style='margin: 0'>In this video, we will understand the neuron.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Does a Model 'Learn'?</p><p style='margin: 0'>In this video, we will understand how a model 'learns'.</p></td></tr><tr><td style='padding: 15px;'><p>8. Making Predictions</p><p style='margin: 0'>In this video, we will be talking about another important part of creating a model, which is making predictions.</p></td></tr><tr><td style='padding: 15px;'><p>9. Saving and Loading a Model</p><p style='margin: 0'>In this video, you will learn how to save a model and then load it later.</p></td></tr><tr><td style='padding: 15px;'><p>10. Why Keras?</p><p style='margin: 0'>In this video, we will understand why we use Keras in this course and not TensorFlow 2.</p></td></tr><tr><td style='padding: 15px;'><p>11. Suggestion Box</p><p style='margin: 0'>In this video, we will have a look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Feedforward Artificial Neural Networks</h4><p>In this section, we will cover feedforward artificial neural networks.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Artificial Neural Networks Section Introduction</p><p style='margin: 0'>In this video, we will get introduced to artificial neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forward Propagation</p><p style='margin: 0'>In this video, you will learn about forward propagation as it is related to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Geometrical Picture</p><p style='margin: 0'>In this video, we will understand the geometrical picture by understanding why neural networks are so important instead of a single neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Activation Functions</p><p style='margin: 0'>In this video, you will learn about activation functions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Multiclass Classification</p><p style='margin: 0'>In this video, you will learn about multiclass classification.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Represent Images</p><p style='margin: 0'>In this video, we will discuss data and understand how to represent images.</p></td></tr><tr><td style='padding: 15px;'><p>7. Code Preparation (Artificial Neural Networks)</p><p style='margin: 0'>In this video, we will work with the MNIST dataset and ANN code preparation.</p></td></tr><tr><td style='padding: 15px;'><p>8. ANN for Image Classification</p><p style='margin: 0'>In this video, we will work on image classification with the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>9. ANN for Regression</p><p style='margin: 0'>In this video, we will understand regression in ANN.</p></td></tr><tr><td style='padding: 15px;'><p>10. How to Choose Hyperparameters</p><p style='margin: 0'>In this video, we will understand how to choose hyperparameters.</p></td></tr></tbody></table><br><h4>4. In-Depth: Loss Functions</h4><p>In this optional section, we will dive deeper into loss functions.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mean Squared Error</p><p style='margin: 0'>In this video, we will understand MSE (Mean Squared Error) from a probabilistic perspective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Binary Cross Entropy</p><p style='margin: 0'>In this video, you will learn about binary cross entropy, which is the correct loss function to use for binary classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Categorical Cross Entropy</p><p style='margin: 0'>In this video, you will learn about categorical cross entropy, which is used in multiclass classification.</p></td></tr></tbody></table><br><h4>5. In-Depth: Gradient Descent</h4><p>In this optional section, we will dive deeper into gradient descent.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Gradient Descent</p><p style='margin: 0'>In this video, we will get introduced to gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>2. Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about stochastic gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>3. Momentum</p><p style='margin: 0'>In this video, you will learn about momentum.</p></td></tr><tr><td style='padding: 15px;'><p>4. Variable and Adaptive Learning Rates</p><p style='margin: 0'>In this video, we will discuss variable and adaptive learning rates.</p></td></tr><tr><td style='padding: 15px;'><p>5. Adam Optimization (Part 1)</p><p style='margin: 0'>In this video, we will talk about Adam optimization, get introduced to Adam, and understand its basics.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adam Optimization (Part 2)</p><p style='margin: 0'>In this video, we will understand more about Adam optimization.</p></td></tr></tbody></table>

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.
TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning. In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization. By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning.
What You Will Learn
Understand what machine learning is
Build linear models with TensorFlow 2
Learn how to build deep neural networks with TensorFlow 2
Learn how to perform image classification and regression with ANN
Learn loss functions such as mean-squared error and cross-entropy loss
Learn about stochastic gradient descent, momentum, and Adam optimization
Audience
This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers.

One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib.
Approach
In this self-paced course, you will learn how to use TensorFlow 2 to build artificial neural networks. The course is well-balanced with theory that explains the ANN concepts and hands-on coding exercises for practical understanding.
Key Features
Understand the utilization of TensorFlow 2 to construct artificial neural networks * The course covers the basics of machine learning, classification, and regression * Explore the connection between artificial neural networks and biological neural networks
About the Author
Lazy Programmer
The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.
Course Outline
1. Welcome

1. Introduction
In this video, we will introduce the author and understand the course learning objective.
1. Introduction
In this video, we will introduce the author and understand the course learning objective.
2. Outline
In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.
2. Outline
In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.
2. Machine Learning and Neurons
In this section, we will cover a crash course on machine learning and neurons. You will learn about classification and regression, and their TensorFlow implementations.
1. What Is Machine Learning?
In this video, we will understand what machine learning is and get a clear idea about ML (machine learning).
2. Code Preparation (Classification Theory)
In this video, we will take a crash course in linear classification for TensorFlow 2.0.
3. Classification Notebook
In this video, we will understand linear classification with the help of breast cancer dataset example.
4. Code Preparation (Regression Theory)
In this video, we will take a crash course in linear regression for TensorFlow 2.0.
5. Regression Notebook
In this video, we will understand linear regression by proving the Mosse law true.
6. The Neuron
In this video, we will understand the neuron.
7. How Does a Model 'Learn'?
In this video, we will understand how a model 'learns'.
8. Making Predictions
In this video, we will be talking about another important part of creating a model, which is making predictions.
9. Saving and Loading a Model
In this video, you will learn how to save a model and then load it later.
10. Why Keras?
In this video, we will understand why we use Keras in this course and not TensorFlow 2.
11. Suggestion Box
In this video, we will have a look at the suggestion box where we can add feedback for this course.
3. Feedforward Artificial Neural Networks
In this section, we will cover feedforward artificial neural networks.
1. Artificial Neural Networks Section Introduction
In this video, we will get introduced to artificial neural networks.
2. Forward Propagation
In this video, you will learn about forward propagation as it is related to neural networks.
3. The Geometrical Picture
In this video, we will understand the geometrical picture by understanding why neural networks are so important instead of a single neural network.
4. Activation Functions
In this video, you will learn about activation functions.
5. Multiclass Classification
In this video, you will learn about multiclass classification.
6. How to Represent Images
In this video, we will discuss data and understand how to represent images.
7. Code Preparation (Artificial Neural Networks)
In this video, we will work with the MNIST dataset and ANN code preparation.
8. ANN for Image Classification
In this video, we will work on image classification with the MNIST dataset.
9. ANN for Regression
In this video, we will understand regression in ANN.
10. How to Choose Hyperparameters
In this video, we will understand how to choose hyperparameters.
4. In-Depth: Loss Functions
In this optional section, we will dive deeper into loss functions.
1. Mean Squared Error
In this video, we will understand MSE (Mean Squared Error) from a probabilistic perspective.
2. Binary Cross Entropy
In this video, you will learn about binary cross entropy, which is the correct loss function to use for binary classification.
3. Categorical Cross Entropy
In this video, you will learn about categorical cross entropy, which is used in multiclass classification.
5. In-Depth: Gradient Descent
In this optional section, we will dive deeper into gradient descent.
1. Gradient Descent
In this video, we will get introduced to gradient descent.
2. Stochastic Gradient Descent
In this video, you will learn about stochastic gradient descent.
3. Momentum
In this video, you will learn about momentum.
4. Variable and Adaptive Learning Rates
In this video, we will discuss variable and adaptive learning rates.
5. Adam Optimization (Part 1)
In this video, we will talk about Adam optimization, get introduced to Adam, and understand its basics.
6. Adam Optimization (Part 2)
In this video, we will understand more about Adam optimization.

Packt

Data Science

Python

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Deep Learning - Artificial Neural Networks with TensorFlow

General Admission

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.</strong>
        <p>Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.

TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.

In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock 'price' predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks.

By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.</p>
        <h2>What You Will Learn</h2><p>Learn about simple RNNs (Elman unit)<br>Covers GRU (gated recurrent unit)<br>Learn how to use LSTM (long short-term memory unit)<br>Learn how to preform time series forecasting<br>Learn how to predict stock price and stock return with LSTM<br>Learn how to apply RNNs to NLP</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks. The course is well-balanced with theory that explains the RNN concepts and hands-on coding exercises for practical understanding. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.</p>
        <h2>Key Features</h2><p>Build your own RNNs with TensorFlow 2 * Explains RNNs, time series, and sequence data * Preform text preprocessing and text classification with LSTMs</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will get introduced to recurrent neural networks and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Recurrent Neural Networks (RNNs), Time Series, and Sequence Data</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Sequence Data</p><p style='margin: 0'>In this video, you will learn about sequence data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forecasting</p><p style='margin: 0'>In this video, we will discuss about forecasting.</p></td></tr><tr><td style='padding: 15px;'><p>3. Autoregressive Linear Model for Time Series Prediction</p><p style='margin: 0'>In this video, we will dive into coding and learn about the autoregressive linear model for time series prediction.</p></td></tr><tr><td style='padding: 15px;'><p>4. Proof That the Linear Model Works</p><p style='margin: 0'>In this video, you will learn how the linear model is about to forecast for a sine wave.</p></td></tr><tr><td style='padding: 15px;'><p>5. Recurrent Neural Networks (Elman Unit Part 1)</p><p style='margin: 0'>In this video, we will get introduced to simple recurrent neural networks also called as Elman Unit.</p></td></tr><tr><td style='padding: 15px;'><p>6. Recurrent Neural Networks (Elman Unit Part 2)</p><p style='margin: 0'>In this video, you will learn how to use the simple RNN to solve many-to-one tasks and many-to-many problems.</p></td></tr><tr><td style='padding: 15px;'><p>7. RNN Code Preparation</p><p style='margin: 0'>In this video, we will understand how to write code in TensorFlow 2 for a simple RNN.</p></td></tr><tr><td style='padding: 15px;'><p>8. RNN for Time Series Prediction</p><p style='margin: 0'>In this video, we will do the same forecasting exercise that we did with an autoregressive liner model, but with a simple RNN instead.</p></td></tr><tr><td style='padding: 15px;'><p>9. Paying Attention to Shapes</p><p style='margin: 0'>In this video, we will work on shapes in RNN.</p></td></tr><tr><td style='padding: 15px;'><p>10. GRU and LSTM (Part 1)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called GRU.</p></td></tr><tr><td style='padding: 15px;'><p>11. GRU and LSTM (Part 2)</p><p style='margin: 0'>In this video, you will learn about modern RNN units called LSTM.</p></td></tr><tr><td style='padding: 15px;'><p>12. A More Challenging Sequence</p><p style='margin: 0'>In this video, you will learn how to preform time series prediction with a complex signal.</p></td></tr><tr><td style='padding: 15px;'><p>13. Demo of the Long-Distance Problem</p><p style='margin: 0'>In this video, we will understand how LSTMs work for resolving a long-distance problem.</p></td></tr><tr><td style='padding: 15px;'><p>14. RNN for Image Classification (Theory)</p><p style='margin: 0'>In this video, we will understand how to apply RNN for image classification.</p></td></tr><tr><td style='padding: 15px;'><p>15. RNN for Image Classification (Code)</p><p style='margin: 0'>In this video, you will learn how to preform RNN for image classification on the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>16. Stock Return Predictions Using LSTMs (Part 1)</p><p style='margin: 0'>In this video, we will understand why one-step prediction on stock price is misleading and unconventional.</p></td></tr><tr><td style='padding: 15px;'><p>17. Stock Return Predictions Using LSTMs (Part 2)</p><p style='margin: 0'>In this video, we will work on a more conventional prediction called stock return.</p></td></tr><tr><td style='padding: 15px;'><p>18. Stock Return Predictions Using LSTMs (Part 3)</p><p style='margin: 0'>In this video, we will make use of all the data and try to predict whether the price will go up or down.</p></td></tr><tr><td style='padding: 15px;'><p>19. Other Ways to Forecast</p><p style='margin: 0'>In this video, we will discuss some other ways that might generate multi-step forecast.</p></td></tr><tr><td style='padding: 15px;'><p>20. Suggestion Box</p><p style='margin: 0'>In this video, the author shares how you can get in touch with him through the suggestion box.</p></td></tr></tbody></table><br><h4>3. Natural Language Processing (NLP)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Embeddings</p><p style='margin: 0'>In this video, we will understand what embedding is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (NLP)</p><p style='margin: 0'>In this video, you will learn how to write the code to turn a sequence of words into an acceptable format such that they can be converted into a matrix of numbers.</p></td></tr><tr><td style='padding: 15px;'><p>3. Text Preprocessing</p><p style='margin: 0'>In this video, we will work on text preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification with LSTMs</p><p style='margin: 0'>In this video, we will work on text classification with LSTMs.</p></td></tr></tbody></table>

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.
Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions. TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs. In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will apply LSTMs to stock 'price' predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do and how not to make the same mistakes that most blogs and courses make when predicting stocks. By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.
What You Will Learn
Learn about simple RNNs (Elman unit)
Covers GRU (gated recurrent unit)
Learn how to use LSTM (long short-term memory unit)
Learn how to preform time series forecasting
Learn how to predict stock price and stock return with LSTM
Learn how to apply RNNs to NLP
Audience
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.
Approach
In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks. The course is well-balanced with theory that explains the RNN concepts and hands-on coding exercises for practical understanding. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.
Key Features
Build your own RNNs with TensorFlow 2 * Explains RNNs, time series, and sequence data * Preform text preprocessing and text classification with LSTMs
About the Author
Lazy Programmer
The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.
Course Outline
1. Welcome

1. Introduction
In this video, we will get introduced to recurrent neural networks and understand the course learning objective.
2. Outline
In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.
2. Recurrent Neural Networks (RNNs), Time Series, and Sequence Data

1. Sequence Data
In this video, you will learn about sequence data.
2. Forecasting
In this video, we will discuss about forecasting.
3. Autoregressive Linear Model for Time Series Prediction
In this video, we will dive into coding and learn about the autoregressive linear model for time series prediction.
4. Proof That the Linear Model Works
In this video, you will learn how the linear model is about to forecast for a sine wave.
5. Recurrent Neural Networks (Elman Unit Part 1)
In this video, we will get introduced to simple recurrent neural networks also called as Elman Unit.
6. Recurrent Neural Networks (Elman Unit Part 2)
In this video, you will learn how to use the simple RNN to solve many-to-one tasks and many-to-many problems.
7. RNN Code Preparation
In this video, we will understand how to write code in TensorFlow 2 for a simple RNN.
8. RNN for Time Series Prediction
In this video, we will do the same forecasting exercise that we did with an autoregressive liner model, but with a simple RNN instead.
9. Paying Attention to Shapes
In this video, we will work on shapes in RNN.
10. GRU and LSTM (Part 1)
In this video, you will learn about modern RNN units called GRU.
11. GRU and LSTM (Part 2)
In this video, you will learn about modern RNN units called LSTM.
12. A More Challenging Sequence
In this video, you will learn how to preform time series prediction with a complex signal.
13. Demo of the Long-Distance Problem
In this video, we will understand how LSTMs work for resolving a long-distance problem.
14. RNN for Image Classification (Theory)
In this video, we will understand how to apply RNN for image classification.
15. RNN for Image Classification (Code)
In this video, you will learn how to preform RNN for image classification on the MNIST dataset.
16. Stock Return Predictions Using LSTMs (Part 1)
In this video, we will understand why one-step prediction on stock price is misleading and unconventional.
17. Stock Return Predictions Using LSTMs (Part 2)
In this video, we will work on a more conventional prediction called stock return.
18. Stock Return Predictions Using LSTMs (Part 3)
In this video, we will make use of all the data and try to predict whether the price will go up or down.
19. Other Ways to Forecast
In this video, we will discuss some other ways that might generate multi-step forecast.
20. Suggestion Box
In this video, the author shares how you can get in touch with him through the suggestion box.
3. Natural Language Processing (NLP)

1. Embeddings
In this video, we will understand what embedding is.
2. Code Preparation (NLP)
In this video, you will learn how to write the code to turn a sequence of words into an acceptable format such that they can be converted into a matrix of numbers.
3. Text Preprocessing
In this video, we will work on text preprocessing.
4. Text Classification with LSTMs
In this video, we will work on text classification with LSTMs.

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

Deep Learning - Recurrent Neural Networks with TensorFlow

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.</strong>
        <p>TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs.

In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision.

By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.</p>
        <h2>What You Will Learn</h2><p>Understand the concept of convolution<br>Integrate convolution into neural networks<br>Apply CNNs to several image recognition datasets, both small and large<br>Learn best practices for designing CNN architectures<br>Learn about batch normalization and data augmentation<br>Learn how to preform text preprocessing</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2.<br><br>One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks. The course is well-balanced with theory that explains the CNN concepts and hands-on coding exercises for practical understanding.</p>
        <h2>Key Features</h2><p>Learn how to use TensorFlow 2 to build Convolutional Neural Networks (CNNs) * The course covers Natural Language Processing (NLP) and transfer learning for Computer Vision * Explains how to apply CNNs to NLP</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Convolutional Neural Networks (CNNs)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Convolution? (Part 1)</p><p style='margin: 0'>In this video, we will get introduced to the concept of convolution.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Convolution? (Part 2)</p><p style='margin: 0'>In this video, we will look at a new perspective on convolution to understand how it works.</p></td></tr><tr><td style='padding: 15px;'><p>3. What Is Convolution? (Part 3)</p><p style='margin: 0'>In this video, you will learn how to view convolution as matrix multiplication.</p></td></tr><tr><td style='padding: 15px;'><p>4. Convolution on Color Images</p><p style='margin: 0'>In this video, we will understand convolution on color images.</p></td></tr><tr><td style='padding: 15px;'><p>5. CNN Architecture</p><p style='margin: 0'>In this video, we will understand the CNN architecture.</p></td></tr><tr><td style='padding: 15px;'><p>6. CNN Code Preparation</p><p style='margin: 0'>In this video, we will look at the code preparation we will use for CNN and understand it.</p></td></tr><tr><td style='padding: 15px;'><p>7. CNN for Fashion MNIST</p><p style='margin: 0'>In this video, we will start with implementing CNN with the fashion MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>8. CNN for CIFAR-10</p><p style='margin: 0'>In this video, we will work on image classification with the CIFAR-10 dataset.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Augmentation</p><p style='margin: 0'>In this video, you will learn about data augmentation.</p></td></tr><tr><td style='padding: 15px;'><p>10. Batch Normalization</p><p style='margin: 0'>In this video, you will learn about batch normalization.</p></td></tr><tr><td style='padding: 15px;'><p>11. Improving CIFAR-10 Results</p><p style='margin: 0'>In this video, you will learn how to improve CIFAR-10 results using data augmentation and batch normalization.</p></td></tr><tr><td style='padding: 15px;'><p>12. Suggestion Box</p><p style='margin: 0'>In this video, we will look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Natural Language Processing (NLP)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Embeddings</p><p style='margin: 0'>In this video, we will understand the text as sequence data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (NLP)</p><p style='margin: 0'>In this video, we will look at the code preparation we will use for NLP and understand it.</p></td></tr><tr><td style='padding: 15px;'><p>3. Text Preprocessing</p><p style='margin: 0'>In this video, we will move into action and understand how to do text preprocessing using Colab notebook.</p></td></tr><tr><td style='padding: 15px;'><p>4. CNNs for Text</p><p style='margin: 0'>In this video, we will understand how we can use CNNs for text sequences.</p></td></tr><tr><td style='padding: 15px;'><p>5. Text Classification with CNNs</p><p style='margin: 0'>In this video, we will move into action and work on text classification with CNNs.</p></td></tr></tbody></table><br><h4>4. Transfer Learning for Computer Vision</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Transfer Learning Theory</p><p style='margin: 0'>In this video, we will get introduced to the concept of transfer learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Some Pre-Trained Models (VGG, ResNet, Inception, MobileNet)</p><p style='margin: 0'>In this video, we will understand VGG, ResNet, Inception, and MobileNet.</p></td></tr><tr><td style='padding: 15px;'><p>3. Large Datasets and Data Generators</p><p style='margin: 0'>In this video, you will learn how to work with large image datasets in TensorFlow and Keras with data generators.</p></td></tr><tr><td style='padding: 15px;'><p>4. 2 Approaches to Transfer Learning</p><p style='margin: 0'>In this video, we will discuss the two approaches used for transfer learning, which we will use in the next videos.</p></td></tr><tr><td style='padding: 15px;'><p>5. Transfer Learning Code (Part 1)</p><p style='margin: 0'>In this video, we will dive into action and look at codes for transfer learning with data augmentation.</p></td></tr><tr><td style='padding: 15px;'><p>6. Transfer Learning Code (Part 2)</p><p style='margin: 0'>In this video, we will dive into action and look at codes for transfer learning without data augmentation.</p></td></tr></tbody></table>

In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.
TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs. In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision. By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.
What You Will Learn
Understand the concept of convolution
Integrate convolution into neural networks
Apply CNNs to several image recognition datasets, both small and large
Learn best practices for designing CNN architectures
Learn about batch normalization and data augmentation
Learn how to preform text preprocessing
Audience
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2.

One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.
Approach
In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks. The course is well-balanced with theory that explains the CNN concepts and hands-on coding exercises for practical understanding.
Key Features
Learn how to use TensorFlow 2 to build Convolutional Neural Networks (CNNs) * The course covers Natural Language Processing (NLP) and transfer learning for Computer Vision * Explains how to apply CNNs to NLP
About the Author
Lazy Programmer
The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.
Course Outline
1. Welcome

1. Introduction
In this video, we will introduce the author and understand the course learning objective.
2. Outline
In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.
2. Convolutional Neural Networks (CNNs)

1. What Is Convolution? (Part 1)
In this video, we will get introduced to the concept of convolution.
2. What Is Convolution? (Part 2)
In this video, we will look at a new perspective on convolution to understand how it works.
3. What Is Convolution? (Part 3)
In this video, you will learn how to view convolution as matrix multiplication.
4. Convolution on Color Images
In this video, we will understand convolution on color images.
5. CNN Architecture
In this video, we will understand the CNN architecture.
6. CNN Code Preparation
In this video, we will look at the code preparation we will use for CNN and understand it.
7. CNN for Fashion MNIST
In this video, we will start with implementing CNN with the fashion MNIST dataset.
8. CNN for CIFAR-10
In this video, we will work on image classification with the CIFAR-10 dataset.
9. Data Augmentation
In this video, you will learn about data augmentation.
10. Batch Normalization
In this video, you will learn about batch normalization.
11. Improving CIFAR-10 Results
In this video, you will learn how to improve CIFAR-10 results using data augmentation and batch normalization.
12. Suggestion Box
In this video, we will look at the suggestion box where we can add feedback for this course.
3. Natural Language Processing (NLP)

1. Embeddings
In this video, we will understand the text as sequence data.
2. Code Preparation (NLP)
In this video, we will look at the code preparation we will use for NLP and understand it.
3. Text Preprocessing
In this video, we will move into action and understand how to do text preprocessing using Colab notebook.
4. CNNs for Text
In this video, we will understand how we can use CNNs for text sequences.
5. Text Classification with CNNs
In this video, we will move into action and work on text classification with CNNs.
4. Transfer Learning for Computer Vision

1. Transfer Learning Theory
In this video, we will get introduced to the concept of transfer learning.
2. Some Pre-Trained Models (VGG, ResNet, Inception, MobileNet)
In this video, we will understand VGG, ResNet, Inception, and MobileNet.
3. Large Datasets and Data Generators
In this video, you will learn how to work with large image datasets in TensorFlow and Keras with data generators.
4. 2 Approaches to Transfer Learning
In this video, we will discuss the two approaches used for transfer learning, which we will use in the next videos.
5. Transfer Learning Code (Part 1)
In this video, we will dive into action and look at codes for transfer learning with data augmentation.
6. Transfer Learning Code (Part 2)
In this video, we will dive into action and look at codes for transfer learning without data augmentation.

Computer Vision

In this self-paced course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNNs). You will learn how to apply CNNs to several practical image recognition datasets and learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning.

Deep Learning - Convolutional Neural Networks with TensorFlow

<strong>This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.</strong>
        <p>Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.

The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach.

You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system.

Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems.

All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        <h2>What You Will Learn</h2><p>Learn about deep learning and recommender systems<br>Explore the mechanisms of deep learning-based approaches<br>Learn to implement a two-tower model for recommenders<br>Implement TensorFlow to develop a recommender system<br>Learn basic neural network models for recommendations<br>Explore neural collaborative filtering and variational autoencoders</p>
        <h2>Audience</h2><p>This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.</p>
        <h2>Approach</h2><p>The course is well-structured and explanatory. Every module has engaging content covering necessary theoretical concepts with practical explanations. The lectures are divided into many videos and comprehensively detailed code notebooks, providing a unique hands-on experience using a real-time project. This course is easily understandable, expressive, and self-explanatory, with live coding.</p>
        <h2>Key Features</h2><p>Understand, implement, and evaluate deep learning models for building real-world recommendation systems * Validate, test, and make predictions using recommender systems with the help of TensorFlow Recommenders * Explore the benefits and challenges of deep learning in recommender systems</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning</p>
        
        <h2>Course Outline</h2><h4>1. Introduction</h4><p>This section focuses on introducing the author and presenting a generalized overview of the course content.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. About the Instructor</p><p style='margin: 0'>In this video, we will get a brief outline of the author, AI Sciences, and how they deliver their courses.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Outline</p><p style='margin: 0'>This brief introductory video will cover the course contents and the concepts you will learn in this course.</p></td></tr></tbody></table><br><h4>2. Deep Learning Foundation for Recommender Systems</h4><p>This section focuses on deep learning methodologies used in recommender systems. Here, we will cover inferences, embeddings, and neutral collaborative filtering aside from VAE and deep learning models.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>In this video, we will look at a general introduction to deep learning and what is covered in this section under deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Overview</p><p style='margin: 0'>This video briefly overviews the contents and topics covered in this module, including deep learning in recommender systems, the pros and cons of deep learning, recommender interference, and more.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deep Learning in Recommender Systems</p><p style='margin: 0'>In this video, we will explore deep learning concepts in recommender systems, migrating from machine learning to deep learning, and capturing non-linear to non-trivial relationships.</p></td></tr><tr><td style='padding: 15px;'><p>4. Inference after Training</p><p style='margin: 0'>In this lecture, you will learn how to deploy the model as a service to infer the likelihood of new interactions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Inference Mechanism</p><p style='margin: 0'>In this lesson, we will understand the generic recommendation systems interference mechanism.</p></td></tr><tr><td style='padding: 15px;'><p>6. Embeddings and User Context</p><p style='margin: 0'>This video focuses on collaborative filtering with the help of deep learning and neural collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>7. Neural Collaborative Filtering</p><p style='margin: 0'>In this video, we will understand neural collaborative filtering and how to incorporate it into our recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>8. VAE Collaborative Filtering</p><p style='margin: 0'>This lesson explores the variational autoencoder for collaborative filtering, using representations in hidden layers.</p></td></tr><tr><td style='padding: 15px;'><p>9. Strengths and Weaknesses of DL Models</p><p style='margin: 0'>In this lesson, you will learn about the strengths and weaknesses of deep learning recommender system models.</p></td></tr><tr><td style='padding: 15px;'><p>10. Deep Learning Quiz</p><p style='margin: 0'>This quiz is based on the deep learning concepts learned so far in this module.</p></td></tr><tr><td style='padding: 15px;'><p>11. Deep Learning Quiz Solution</p><p style='margin: 0'>This is the solution to the quiz based on the deep learning concepts learned so far in this module.</p></td></tr></tbody></table><br><h4>3. Project Amazon Product Recommendation System</h4><p>This module focuses on TensorFlow recommenders, creating a two-tower model, data visualization with WordCloud, creating tensors with DataFrame, query towers, random train test split, and computer loss.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Overview</p><p style='margin: 0'>This is a brief introductory overview of what is being covered in this section, including creating projects, TensorFlow recommenders, libraries, WordCloud, and DataFrame.</p></td></tr><tr><td style='padding: 15px;'><p>2. TensorFlow Recommenders</p><p style='margin: 0'>In this video, you will learn about TensorFlow recommenders used to develop recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Two-Tower Model</p><p style='margin: 0'>In this lesson, we will discuss the two-tower model, which uses user embedding and item embedding in the recommender system.</p></td></tr><tr><td style='padding: 15px;'><p>4. Project Overview</p><p style='margin: 0'>This video outlines the project and will demonstrate how to install the packages required for the project, data preparation, using TensorFlow, evaluation, and recommendation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Download Libraries</p><p style='margin: 0'>In this video, you will learn to implement the Amazon recommender system using the TensorFlow recommender based on specific user behavior. You will learn to download and import particular libraries for the project.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Visualization with WordCloud</p><p style='margin: 0'>In this video, you will learn how to load a dataset for the project being developed, using WordCloud, importing WordCloud, STOPWORDS, and ImageColorGenerator.</p></td></tr><tr><td style='padding: 15px;'><p>7. Make Tensors from DataFrame</p><p style='margin: 0'>In this video, we will continue to check our dataset using a single username. We will use a part of the DataFrame to do so.</p></td></tr><tr><td style='padding: 15px;'><p>8. Rating Our Data</p><p style='margin: 0'>In this video, you will learn about the next part of the project, which is rating our data.</p></td></tr><tr><td style='padding: 15px;'><p>9. Random Train-Test Split</p><p style='margin: 0'>In this lesson, you will learn to perform the test train split, which will do the training split and then create a prediction; we will use a random dataset (80-20 ratio).</p></td></tr><tr><td style='padding: 15px;'><p>10. Making the Model and Query Tower</p><p style='margin: 0'>In this lecture, you will learn to develop our model and create a query tower to perform retrieval tasks.</p></td></tr><tr><td style='padding: 15px;'><p>11. Candidate Tower and Retrieval System</p><p style='margin: 0'>In this video, you will learn how to create a candidate tower and develop a retrieval system.</p></td></tr><tr><td style='padding: 15px;'><p>12. Compute Loss</p><p style='margin: 0'>Here, we will look at the next step of training our model: the compute loss function.</p></td></tr><tr><td style='padding: 15px;'><p>13. Train and Validation</p><p style='margin: 0'>After entirely developing our model, you will learn to fit and evaluate the model for functionality.</p></td></tr><tr><td style='padding: 15px;'><p>14. Accuracy Versus Recommendations</p><p style='margin: 0'>In this video, we will perform data visualization with the project we created and check the model's accuracy.</p></td></tr><tr><td style='padding: 15px;'><p>15. Making Recommendations</p><p style='margin: 0'>In this lesson, you will learn to create a recommendation and use a brute-force algorithm to generate the recommendation.</p></td></tr></tbody></table>

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.
Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for products recommendation, prepare data, and model development using a two-tower approach. You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system. Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems. All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning
What You Will Learn
Learn about deep learning and recommender systems
Explore the mechanisms of deep learning-based approaches
Learn to implement a two-tower model for recommenders
Implement TensorFlow to develop a recommender system
Learn basic neural network models for recommendations
Explore neural collaborative filtering and variational autoencoders
Audience
This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. Individuals passionate about recommender systems with the help of TensorFlow Recommenders will benefit from this course. Deep learning practitioners, research scholars, and data scientists will also benefit from the course. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.
Approach
The course is well-structured and explanatory. Every module has engaging content covering necessary theoretical concepts with practical explanations. The lectures are divided into many videos and comprehensively detailed code notebooks, providing a unique hands-on experience using a real-time project. This course is easily understandable, expressive, and self-explanatory, with live coding.
Key Features
Understand, implement, and evaluate deep learning models for building real-world recommendation systems * Validate, test, and make predictions using recommender systems with the help of TensorFlow Recommenders * Explore the benefits and challenges of deep learning in recommender systems
Github Repo
https://github.com/PacktPublishing/Recommender-Systems-An-Applied-Approach-using-Deep-Learning
Course Outline
1. Introduction
This section focuses on introducing the author and presenting a generalized overview of the course content.
1. About the Instructor
In this video, we will get a brief outline of the author, AI Sciences, and how they deliver their courses.
2. Course Outline
This brief introductory video will cover the course contents and the concepts you will learn in this course.
2. Deep Learning Foundation for Recommender Systems
This section focuses on deep learning methodologies used in recommender systems. Here, we will cover inferences, embeddings, and neutral collaborative filtering aside from VAE and deep learning models.
1. Module Introduction
In this video, we will look at a general introduction to deep learning and what is covered in this section under deep learning.
2. Overview
This video briefly overviews the contents and topics covered in this module, including deep learning in recommender systems, the pros and cons of deep learning, recommender interference, and more.
3. Deep Learning in Recommender Systems
In this video, we will explore deep learning concepts in recommender systems, migrating from machine learning to deep learning, and capturing non-linear to non-trivial relationships.
4. Inference after Training
In this lecture, you will learn how to deploy the model as a service to infer the likelihood of new interactions.
5. Inference Mechanism
In this lesson, we will understand the generic recommendation systems interference mechanism.
6. Embeddings and User Context
This video focuses on collaborative filtering with the help of deep learning and neural collaborative filtering.
7. Neural Collaborative Filtering
In this video, we will understand neural collaborative filtering and how to incorporate it into our recommender system.
8. VAE Collaborative Filtering
This lesson explores the variational autoencoder for collaborative filtering, using representations in hidden layers.
9. Strengths and Weaknesses of DL Models
In this lesson, you will learn about the strengths and weaknesses of deep learning recommender system models.
10. Deep Learning Quiz
This quiz is based on the deep learning concepts learned so far in this module.
11. Deep Learning Quiz Solution
This is the solution to the quiz based on the deep learning concepts learned so far in this module.
3. Project Amazon Product Recommendation System
This module focuses on TensorFlow recommenders, creating a two-tower model, data visualization with WordCloud, creating tensors with DataFrame, query towers, random train test split, and computer loss.
1. Module Overview
This is a brief introductory overview of what is being covered in this section, including creating projects, TensorFlow recommenders, libraries, WordCloud, and DataFrame.
2. TensorFlow Recommenders
In this video, you will learn about TensorFlow recommenders used to develop recommender systems.
3. Two-Tower Model
In this lesson, we will discuss the two-tower model, which uses user embedding and item embedding in the recommender system.
4. Project Overview
This video outlines the project and will demonstrate how to install the packages required for the project, data preparation, using TensorFlow, evaluation, and recommendation.
5. Download Libraries
In this video, you will learn to implement the Amazon recommender system using the TensorFlow recommender based on specific user behavior. You will learn to download and import particular libraries for the project.
6. Data Visualization with WordCloud
In this video, you will learn how to load a dataset for the project being developed, using WordCloud, importing WordCloud, STOPWORDS, and ImageColorGenerator.
7. Make Tensors from DataFrame
In this video, we will continue to check our dataset using a single username. We will use a part of the DataFrame to do so.
8. Rating Our Data
In this video, you will learn about the next part of the project, which is rating our data.
9. Random Train-Test Split
In this lesson, you will learn to perform the test train split, which will do the training split and then create a prediction; we will use a random dataset (80-20 ratio).
10. Making the Model and Query Tower
In this lecture, you will learn to develop our model and create a query tower to perform retrieval tasks.
11. Candidate Tower and Retrieval System
In this video, you will learn how to create a candidate tower and develop a retrieval system.
12. Compute Loss
Here, we will look at the next step of training our model: the compute loss function.
13. Train and Validation
After entirely developing our model, you will learn to fit and evaluate the model for functionality.
14. Accuracy Versus Recommendations
In this video, we will perform data visualization with the project we created and check the model's accuracy.
15. Making Recommendations
In this lesson, you will learn to create a recommendation and use a brute-force algorithm to generate the recommendation.

Data Analysis

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.

Recommender Systems: An Applied Approach using Deep Learning

<strong>This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.</strong>
        <p>With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority.

This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow.

The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications.

The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks</p>
        <h2>What You Will Learn</h2><p>Gain an overview of deep neural networks<br>Understand the fundamentals of RNN architectures<br>Train real-world datasets using different RNN architectures<br>Implement RNNs, LSTM, and GRUs through hands-on exercises<br>Create and compile RNN models in TensorFlow<br>Perform text classification using RNNs and TensorFlow</p>
        <h2>Audience</h2><p>As this course begins with the basics, no prior knowledge in RNNs is required. However, prior experience in Python would be beneficial. Whether you are a beginner, a seasoned data scientist looking to get started with RNNs, business analysts, or if you simply want to implement RNNs in your projects, this course is for you.</p>
        <h2>Approach</h2><p>Through carefully designed modules, a simple-to-understand theory, engaging hands-on exercises, and realistic implementations of RNNs in projects, this course will help you master RNNs.</p>
        <h2>Key Features</h2><p>Understand and apply fundamentals of recurrent neural networks * Implement RNNs and related architectures on real-world datasets * Train RNNs for real-world applications-automatic book writer and stock price prediction</p>
        <h2>Github Repo</h2><p>https://github.com/AISCIENCES/mastering_recurrent_neural_networks</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Applications of RNN</h4><p></p><br><h4>3. Deep Neural Network (DNN) Overview</h4><p></p><br><h4>4. RNN Architecture</h4><p></p><br><h4>5. Gradient Descent in RNN</h4><p></p><br><h4>6. RNN Implementation</h4><p></p><br><h4>7. Sentiment Classification Using RNN</h4><p></p><br><h4>8. Vanishing Gradients in RNN</h4><p></p><br><h4>9. TensorFlow</h4><p></p><br><h4>10. Project 1: Book Writer</h4><p></p><br><h4>11. Project 2: Stock Price Prediction</h4><p></p><br><h4>12. Further Reading and Resources</h4><p></p>

This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.
With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority. This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow. The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications. The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks
What You Will Learn
Gain an overview of deep neural networks
Understand the fundamentals of RNN architectures
Train real-world datasets using different RNN architectures
Implement RNNs, LSTM, and GRUs through hands-on exercises
Create and compile RNN models in TensorFlow
Perform text classification using RNNs and TensorFlow
Audience
As this course begins with the basics, no prior knowledge in RNNs is required. However, prior experience in Python would be beneficial. Whether you are a beginner, a seasoned data scientist looking to get started with RNNs, business analysts, or if you simply want to implement RNNs in your projects, this course is for you.
Approach
Through carefully designed modules, a simple-to-understand theory, engaging hands-on exercises, and realistic implementations of RNNs in projects, this course will help you master RNNs.
Key Features
Understand and apply fundamentals of recurrent neural networks * Implement RNNs and related architectures on real-world datasets * Train RNNs for real-world applications-automatic book writer and stock price prediction
Github Repo
https://github.com/AISCIENCES/mastering_recurrent_neural_networks
About the Author
AI Sciences
AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.
Course Outline
1. Introduction

2. Applications of RNN

3. Deep Neural Network (DNN) Overview

4. RNN Architecture

5. Gradient Descent in RNN

6. RNN Implementation

7. Sentiment Classification Using RNN

8. Vanishing Gradients in RNN

9. TensorFlow

10. Project 1: Book Writer

11. Project 2: Stock Price Prediction

12. Further Reading and Resources


Deep Learning

This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.

Deep Learning: Recurrent Neural Networks with Python

<strong>Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.</strong>
        <p>Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks.

This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas.

With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications.

Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results.

By the end of the course, you will be well on your way to becoming a deep learning expert in no time.</p>
        <h2>What You Will Learn</h2><p>Learn about the fundamentals of Python and some of its well-known libraries<br>Understand the fundamentals of deep learning and neural networks<br>Build and train your own deep neural network models<br>Learn different activation functions and optimization algorithms<br>Learn techniques for improving model performance and reducing overfitting<br>Apply deep learning to real-world problems in various fields</p>
        <h2>Audience</h2><p>This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.</p>
        <h2>Approach</h2><p>The course adopts a practical, hands-on learning approach that focuses on building intuition and mastery through implementation. Students will work through a series of exercises and projects designed to help them understand the fundamental concepts of deep learning and develop their skills in Python programming, data analysis, and neural network design.</p>
        <h2>Key Features</h2><p>Practical hands-on exercises to build intuition on deep neural networks * Learn essential concepts in deep learning through practical applications * A comprehensive understanding of neural network architecture and design</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Deep-Learning---Crash-Course-2023</p>
        <h2>About the Author</h2><h3>Manifold AI Learning</h3><p>Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence.

With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><br><h4>1. Welcome on Board</h4><p></p><br><h4>2. Getting the Basics Right</h4><p></p><br><h4>3. Python Crash Course on Basics</h4><p></p><br><h4>4. Python for Data Science - Crash Course</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>5. MP Neuron Model</h4><p></p><br><h4>6. MP Neuron in Python</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>7. Summary of MP Neuron</h4><p></p><br><h4>8. Perceptron</h4><p></p><br><h4>9. Perceptron in Python</h4><p></p><br><h4>10. Sigmoid Neuron</h4><p></p><br><h4>11. Sigmoid Neuron Implement with Python</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>12. Basic Probability</h4><p></p><br><h4>13. Why Deep Neural Networks - Intuition</h4><p></p><br><h4>13. Deep Neural Networks</h4><p></p><br><h4>14. Universal Approximation Theorem</h4><p></p><br><h4>15. Deep Learning with TensorFlow 2.x</h4><p></p><br><h4>16. Activation Functions in Deep Learning Neural Networks</h4><p></p><br><h4>17. Applying Deep Learning</h4><p></p>

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.
Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks. This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas. With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications. Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results. By the end of the course, you will be well on your way to becoming a deep learning expert in no time.
What You Will Learn
Learn about the fundamentals of Python and some of its well-known libraries

Understand the fundamentals of deep learning and neural networks

Build and train your own deep neural network models

Learn different activation functions and optimization algorithms

Learn techniques for improving model performance and reducing overfitting

Apply deep learning to real-world problems in various fields
Audience
This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.
Approach
The course adopts a practical, hands-on learning approach that focuses on building intuition and mastery through implementation. Students will work through a series of exercises and projects designed to help them understand the fundamental concepts of deep learning and develop their skills in Python programming, data analysis, and neural network design.
Key Features
Practical hands-on exercises to build intuition on deep neural networks * Learn essential concepts in deep learning through practical applications * A comprehensive understanding of neural network architecture and design
Github Repo
https://github.com/PacktPublishing/Deep-Learning---Crash-Course-2023
About the Author
Manifold AI Learning
Manifold AI Learning is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence. With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.
Course Outline
1. Welcome

1. Welcome on Board

2. Getting the Basics Right

3. Python Crash Course on Basics

4. Python for Data Science - Crash Course

5. MP Neuron Model

5. MP Neuron Model

6. MP Neuron in Python

7. Summary of MP Neuron

7. Summary of MP Neuron

8. Perceptron

9. Perceptron in Python

10. Sigmoid Neuron

11. Sigmoid Neuron Implement with Python

12. Basic Probability

12. Basic Probability

13. Why Deep Neural Networks - Intuition

13. Deep Neural Networks

14. Universal Approximation Theorem

15. Deep Learning with TensorFlow 2.x

16. Activation Functions in Deep Learning Neural Networks

17. Applying Deep Learning


Keras

Pandas

Kickstart your journey into deep learning and gain a strong understanding of deep neural networks through practical exercises. Develop your intuition and learn the fundamentals of artificial neural networks, activation functions, and loss functions. Gain practical experience with Python and TensorFlow 2.x, and apply your skills to build powerful deep learning models.

Deep Learning - Crash Course 2023

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

Building Recommender Systems with Machine Learning and AI

<strong>This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and learn how to use it to develop and evaluate deep learning models.</strong>
        <p>Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it.

There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models.

This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models.

All resources and code files for this course are placed here: https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers</p>
        <h2>What You Will Learn</h2><p>Develop and evaluate neural network models end-to-end<br>Build larger models for image and text data<br>Understand the anatomy of a Keras model<br>Evaluate the performance of a deep learning Keras model<br>Build end-to-end regression and classification models in Keras<br>Learn how to use checkpointing to save the best model run</p>
        <h2>Audience</h2><p>This course is for developers, machine learning engineers, and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts such as cross-validation and one-hot encoding used in lessons and projects are described, but only briefly. With all of this in mind, this is an entry-level course on the Keras library.</p>
        <h2>Approach</h2><p>The course follows a hands-on approach towards learning. To get the most out of the course, it is recommended to work through all the examples in each tutorial.</p>
        <h2>Key Features</h2><p>Learn how to use more advanced techniques required to develop state-of-the-art deep learning models * Learn how to use advanced image augmentation techniques in order to lift model performance * Learn how to enhance performance with learning rate schedules</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers</p>
        <h2>About the Author</h2><h3>Mike West</h3><p>Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman.

Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Foundations</h4><p></p><br><h4>3. Going Deeper with Keras</h4><p></p><br><h4>4. Convolutional Neural Networks</h4><p></p><br><h4>5. Recurrent Neural Networks</h4><p></p>

This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and learn how to use it to develop and evaluate deep learning models.
Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it. There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models. This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models. All resources and code files for this course are placed here: https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers
What You Will Learn
Develop and evaluate neural network models end-to-end
Build larger models for image and text data
Understand the anatomy of a Keras model
Evaluate the performance of a deep learning Keras model
Build end-to-end regression and classification models in Keras
Learn how to use checkpointing to save the best model run
Audience
This course is for developers, machine learning engineers, and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts such as cross-validation and one-hot encoding used in lessons and projects are described, but only briefly. With all of this in mind, this is an entry-level course on the Keras library.
Approach
The course follows a hands-on approach towards learning. To get the most out of the course, it is recommended to work through all the examples in each tutorial.
Key Features
Learn how to use more advanced techniques required to develop state-of-the-art deep learning models * Learn how to use advanced image augmentation techniques in order to lift model performance * Learn how to enhance performance with learning rate schedules
Github Repo
https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers
About the Author
Mike West
Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman. Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.
Course Outline
1. Introduction

2. Foundations

3. Going Deeper with Keras

4. Convolutional Neural Networks

5. Recurrent Neural Networks


This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and learn how to use it to develop and evaluate deep learning models.

Hands-On Keras for Machine Learning Engineers

<strong>Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.</strong>
        <p>Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM).

You will also have code blocks and notebooks to help you understand the topics covered in the course.

By the end of this course, you will have a hands-on understanding of Neural Networks in detail.

All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks</p>
        <h2>What You Will Learn</h2><p>Learn about linear and logistic regression in ANN<br>Learn about cross-entropy between two probability distributions<br>Understand convolution operation which scans inputs with respect to their dimensions<br>Understand VGG16, a convolutional neural network model<br>Understand why to use recurrent neural network<br>Understand Long short-term memory (LSTM)</p>
        <h2>Audience</h2><p>This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab.</p>
        <h2>Approach</h2><p>This course contains a detailed discussion on various topics of Deep Learning, mathematical description, and code walkthroughs of the three common families of neural networks.</p>
        <h2>Key Features</h2><p>Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks Understand backward and forward propagation in ANN Understand Bidirectional Recurrent Neural Networks (BRNN)</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks</p>
        <h2>About the Author</h2><h3>Yiqiao Yin</h3><p>Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP.

Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome Message</p><p style='margin: 0'>This video explains the need for taking up the course and introduces you to the author.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Outline</p><p style='margin: 0'>This video explains the course outline and what the course has to offer.</p></td></tr></tbody></table><br><h4>2. Artificial Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression</p><p style='margin: 0'>This video explains statistical machine learning, where you will start with the linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression</p><p style='margin: 0'>This video explains logistic regression and specifically if the target here is discrete or binary.</p></td></tr><tr><td style='padding: 15px;'><p>3. Purpose of Neural Networks</p><p style='margin: 0'>This video explains the purpose of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Forward Propagation</p><p style='margin: 0'>This video explains forward propagation and will dive deeper into the architecture of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Backward Propagation</p><p style='margin: 0'>This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>6. Activation Function</p><p style='margin: 0'>This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. Cross-Entropy Loss Function</p><p style='margin: 0'>This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Descent</p><p style='margin: 0'>This video explains the optimization problem using the gradient descent algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - Introduction to Python</p><p style='margin: 0'>This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Introduction to TensorFlow - Remove the Throat-Clearing Sound in the Start of the Video</p><p style='margin: 0'>This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects.</p></td></tr><tr><td style='padding: 15px;'><p>11. Lab 3 - Introduction to Neural Network</p><p style='margin: 0'>This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lab 4 - Functional API</p><p style='margin: 0'>This video demonstrates functional API versus sequential API.</p></td></tr><tr><td style='padding: 15px;'><p>13. Lab 5 - Building Deeper and Wider Model</p><p style='margin: 0'>This video demonstrates how to build a deeper and wider neural network model.</p></td></tr></tbody></table><br><h4>3. Convolutional Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Image Data</p><p style='margin: 0'>This video explains image data in CNN (Convolutional Neural Network).</p></td></tr><tr><td style='padding: 15px;'><p>2. Tensor and Matrix</p><p style='margin: 0'>This video explains what we mean by Tensor and Matrix.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolutional Operation</p><p style='margin: 0'>The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature map or activation map.</p></td></tr><tr><td style='padding: 15px;'><p>4. Padding</p><p style='margin: 0'>This video explains padding in convolutional neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Stride</p><p style='margin: 0'>For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation.</p></td></tr><tr><td style='padding: 15px;'><p>6. Convolution in 2D and 3D</p><p style='margin: 0'>This video explains Convolution in 2D and 3D.</p></td></tr><tr><td style='padding: 15px;'><p>7. VGG16</p><p style='margin: 0'>This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition".</p></td></tr><tr><td style='padding: 15px;'><p>8. Residual Network</p><p style='margin: 0'>Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - Introduction to Convolutional 1-Dimensional</p><p style='margin: 0'>This video demonstrates convolutional operations in 1-dimension.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Introduction to CNN</p><p style='margin: 0'>This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network.</p></td></tr><tr><td style='padding: 15px;'><p>11. Lab 3 - Deep CNN</p><p style='margin: 0'>This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lab 4 - Transfer Learning</p><p style='margin: 0'>This video demonstrates transfer learning.</p></td></tr></tbody></table><br><h4>4. Recurrent Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to RNN</p><p style='margin: 0'>This video explains recurrent neural networks and why we want to use RNN.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Use RNN</p><p style='margin: 0'>A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the next likely scenario.</p></td></tr><tr><td style='padding: 15px;'><p>3. Language Processing</p><p style='margin: 0'>NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information.</p></td></tr><tr><td style='padding: 15px;'><p>4. Forward Propagation in RNN</p><p style='margin: 0'>The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters.</p></td></tr><tr><td style='padding: 15px;'><p>5. Backward Propagation Through Time</p><p style='margin: 0'>Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.</p></td></tr><tr><td style='padding: 15px;'><p>6. Gated Recurrent Unit (GRU)</p><p style='margin: 0'>Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.</p></td></tr><tr><td style='padding: 15px;'><p>7. Long Short-Term Memory (LSTM)</p><p style='margin: 0'>Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between important events in a time series.</p></td></tr><tr><td style='padding: 15px;'><p>8. Bi-Directional RNN</p><p style='margin: 0'>Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the performance can be enhanced by knowledge of the letters located before and after the current letter.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - RNN in Text Classification</p><p style='margin: 0'>This video demonstrates how to design a recurrent neural network or RNN.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Sequence to Sequence Stock Candlestick Forecast</p><p style='margin: 0'>This video demonstrates sequence-to-sequence stock candlestick forecast.</p></td></tr></tbody></table>

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.
Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). You will also have code blocks and notebooks to help you understand the topics covered in the course. By the end of this course, you will have a hands-on understanding of Neural Networks in detail. All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks
What You Will Learn
Learn about linear and logistic regression in ANN
Learn about cross-entropy between two probability distributions
Understand convolution operation which scans inputs with respect to their dimensions
Understand VGG16, a convolutional neural network model
Understand why to use recurrent neural network
Understand Long short-term memory (LSTM)
Audience
This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab.
Approach
This course contains a detailed discussion on various topics of Deep Learning, mathematical description, and code walkthroughs of the three common families of neural networks.
Key Features
Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks Understand backward and forward propagation in ANN Understand Bidirectional Recurrent Neural Networks (BRNN)
Github Repo
https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks
About the Author
Yiqiao Yin
Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP. Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.
Course Outline
1. Welcome

1. Welcome Message
This video explains the need for taking up the course and introduces you to the author.
2. Course Outline
This video explains the course outline and what the course has to offer.
2. Artificial Neural Networks

1. Linear Regression
This video explains statistical machine learning, where you will start with the linear regression model.
2. Logistic Regression
This video explains logistic regression and specifically if the target here is discrete or binary.
3. Purpose of Neural Networks
This video explains the purpose of neural networks.
4. Forward Propagation
This video explains forward propagation and will dive deeper into the architecture of neural networks.
5. Backward Propagation
This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm.
6. Activation Function
This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks.
7. Cross-Entropy Loss Function
This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.
8. Gradient Descent
This video explains the optimization problem using the gradient descent algorithm.
9. Lab 1 - Introduction to Python
This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function.
10. Lab 2 - Introduction to TensorFlow - Remove the Throat-Clearing Sound in the Start of the Video
This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects.
11. Lab 3 - Introduction to Neural Network
This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture.
12. Lab 4 - Functional API
This video demonstrates functional API versus sequential API.
13. Lab 5 - Building Deeper and Wider Model
This video demonstrates how to build a deeper and wider neural network model.
3. Convolutional Neural Networks

1. Image Data
This video explains image data in CNN (Convolutional Neural Network).
2. Tensor and Matrix
This video explains what we mean by Tensor and Matrix.
3. Convolutional Operation
The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature map or activation map.
4. Padding
This video explains padding in convolutional neural networks.
5. Stride
For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation.
6. Convolution in 2D and 3D
This video explains Convolution in 2D and 3D.
7. VGG16
This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition".
8. Residual Network
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
9. Lab 1 - Introduction to Convolutional 1-Dimensional
This video demonstrates convolutional operations in 1-dimension.
10. Lab 2 - Introduction to CNN
This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network.
11. Lab 3 - Deep CNN
This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters.
12. Lab 4 - Transfer Learning
This video demonstrates transfer learning.
4. Recurrent Neural Networks

1. Welcome to RNN
This video explains recurrent neural networks and why we want to use RNN.
2. Why Use RNN
A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the next likely scenario.
3. Language Processing
NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information.
4. Forward Propagation in RNN
The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters.
5. Backward Propagation Through Time
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.
6. Gated Recurrent Unit (GRU)
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.
7. Long Short-Term Memory (LSTM)
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between important events in a time series.
8. Bi-Directional RNN
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the performance can be enhanced by knowledge of the letters located before and after the current letter.
9. Lab 1 - RNN in Text Classification
This video demonstrates how to design a recurrent neural network or RNN.
10. Lab 2 - Sequence to Sequence Stock Candlestick Forecast
This video demonstrates sequence-to-sequence stock candlestick forecast.

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Fundamentals of Neural Networks

<strong>This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras. </strong>
        <p>Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence (AI)  tasks.

You'll start with an introduction to deep learning where you'll focus on the fundamentals of the deep learning theory and find out how to use deep learning in Python, before moving on to artificial neural networks (ANNs). You'll learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, you'll discover how to make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. The video also covers convolutional neural networks (CNNs) at length and goes through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, you'll wrap up CNN  implementation  in Python.

By the end of this course, you'll be able to use the concepts of deep learning to build neural networks in Python like a professional.</p>
        <h2>What You Will Learn</h2><p>Get to grips with the fundamentals of the deep learning theory<br>Discover how to use deep learning in Python<br>Find out how to use different frameworks in Python<br>Build artificial neural networks with TensorFlow and Keras<br>Explore the implementation of ANNs and CNNs in Python</p>
        <h2>Audience</h2><p>This course is for both programming beginners and professionals who want to expand on their knowledge of deep learning, or professional mathematicians looking to learn how to analyze data programmatically. Prerequisites include basic mathematical skills and Python coding experience.</p>
        <h2>Approach</h2><p>This fundamentals training is excellent for both newcomers and seasoned professionals. This is the best course for you if you're looking for one that starts with the fundamentals and progresses to more complex subjects. It does not teach anything but what is necessary to get started with deep learning. This is pretty much everything you need to get started with the topic, and it also helps to keep the course quite concise.</p>
        <h2>Key Features</h2><p>Fundamentals course designed for both beginners and experts alike * Use different frameworks in Python to solve real-world problems using deep learning and AI * Make predictions using linear regression, polynomial regression, and multivariate regression</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-for-Deep-Learning---Build-Neural-Networks-in-Python</p>
        <h2>About the Author</h2><h3>Meta Brains</h3><p>Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone.

Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!</p>
        <h2>Course Outline</h2><h4>1. Introduction to Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Deep Learning?</p><p style='margin: 0'>In this video, we will understand what deep learning is.</p></td></tr><tr><td style='padding: 15px;'><p>3. Why is Deep Learning Important?</p><p style='margin: 0'>In this video, we will understand the importance of deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Software and Frameworks</p><p style='margin: 0'>In this video, you will learn about software and frameworks.</p></td></tr></tbody></table><br><h4>2. Artificial Neural Networks (ANN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Anatomy and Function of Neurons</p><p style='margin: 0'>In this video, you will learn about the anatomy and function of neurons.</p></td></tr><tr><td style='padding: 15px;'><p>3. An Introduction to The Neural Network</p><p style='margin: 0'>In this video, we will have a quick introduction to the neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Architecture of a Neural Network</p><p style='margin: 0'>In this video, you will learn about the architecture of a neural network.</p></td></tr></tbody></table><br><h4>3. Propagation of Information in ANNs</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Feed-Forward and Back Propagation Networks</p><p style='margin: 0'>In this video, you will learn about feed-forward and back propagation networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Backpropagation in Neural Networks</p><p style='margin: 0'>In this video, you will learn about backpropagation in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Minimizing the Cost Function Using Backpropagation</p><p style='margin: 0'>In this video, you will learn how to minimize the cost function using backpropagation.</p></td></tr></tbody></table><br><h4>4. Neural Network Architectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Single Layer Perceptron (SLP) Model</p><p style='margin: 0'>In this video, you will learn about the Single-layer perceptron (SLP) model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Radial Basis Network (RBN)</p><p style='margin: 0'>In this video, you will learn about Radial Basis Network (RBN).</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-Layer Perceptron (MLP) Neural Network</p><p style='margin: 0'>In this video, you will learn about multi-layer perceptron (MLP) Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Recurrent Neural Network (RNN)</p><p style='margin: 0'>In this video, you will learn about Recurrent Neural Network (RNN).</p></td></tr><tr><td style='padding: 15px;'><p>5. Long Short-Term Memory (LSTM) Networks</p><p style='margin: 0'>In this video, you will learn about Long Short-Term Memory (LSTM) networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Hopfield Neural Network</p><p style='margin: 0'>In this video, you will learn about Hopfield neural network.</p></td></tr><tr><td style='padding: 15px;'><p>7. Boltzmann Machine Neural Network</p><p style='margin: 0'>In this video, you will learn about Boltzmann Machine Neural Network.</p></td></tr></tbody></table><br><h4>5. Activation Functions</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is the Activation Function?</p><p style='margin: 0'>In this video, we will understand what the Activation function is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Important Terminologies</p><p style='margin: 0'>In this video, we will understand some important terminologies like differential function and monotonic function.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Sigmoid Function</p><p style='margin: 0'>In this video, you will learn about the Sigmoid function.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hyperbolic Tangent Function</p><p style='margin: 0'>In this video, you will learn about the Hyperbolic tangent function.</p></td></tr><tr><td style='padding: 15px;'><p>5. SoftMax Function</p><p style='margin: 0'>In this video, you will learn about the SoftMax function.</p></td></tr><tr><td style='padding: 15px;'><p>6. Rectified Linear Unit (ReLU) Function</p><p style='margin: 0'>In this video, you will learn about Rectified Linear Unit (ReLU) function.</p></td></tr><tr><td style='padding: 15px;'><p>7. Leaky Rectified Linear Unit function</p><p style='margin: 0'>In this video, you will learn about the Leaky Rectified Linear Unit function.</p></td></tr></tbody></table><br><h4>6. Gradient Descent Algorithm</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what Gradient Descent is.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Stochastic Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what is Stochastic Gradient Descent?</p></td></tr><tr><td style='padding: 15px;'><p>3. Gradient Descent versus Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about Gradient Decent versus Stochastic Gradient Descent.</p></td></tr></tbody></table><br><h4>7. Summary - Overview of Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How do Artificial Neural Networks Work?</p><p style='margin: 0'>In this video, we will understand how artificial neural networks work.</p></td></tr><tr><td style='padding: 15px;'><p>2. Advantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the advantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Disadvantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the disadvantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Applications of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the applications of neural networks.</p></td></tr></tbody></table><br><h4>8. Implementation of ANN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Exploring the Dataset</p><p style='margin: 0'>In this video, you will learn how to explore the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Problem Statement</p><p style='margin: 0'>In this video, we will understand the problem statement.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Pre-Processing</p><p style='margin: 0'>In this video, you will learn how data pre-processing works.</p></td></tr><tr><td style='padding: 15px;'><p>5. Loading the Dataset</p><p style='margin: 0'>In this video, you will learn how to load the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>6. Splitting the Dataset into Independent and Dependent Variables</p><p style='margin: 0'>In this video, you will learn how to split the dataset into independent and dependent variables.</p></td></tr><tr><td style='padding: 15px;'><p>7. Label Encoding Using Scikit-Learn</p><p style='margin: 0'>In this video, you will learn how to work with label encoding using scikit-learn</p></td></tr><tr><td style='padding: 15px;'><p>8. One-hot encoding using scikit-learn</p><p style='margin: 0'>In this video, we will learn how to work with One-hot encoding using scikit-learn.</p></td></tr><tr><td style='padding: 15px;'><p>9. Training and Test Sets: Splitting Data</p><p style='margin: 0'>In this video, we will be working on splitting the data with training and test sets.</p></td></tr><tr><td style='padding: 15px;'><p>10. Feature Scaling</p><p style='margin: 0'>In this video, you will learn about feature scaling.</p></td></tr><tr><td style='padding: 15px;'><p>11. Building the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to build the Artificial Neural Network</p></td></tr><tr><td style='padding: 15px;'><p>12. Adding the Input Layer and the First Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the input layer and the first hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>13. Adding the Next Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the next hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>14. Adding the Output Layer</p><p style='margin: 0'>In this video, you will learn how to add the output layer.</p></td></tr><tr><td style='padding: 15px;'><p>15. Compiling the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to compile the Artificial Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>16. Fitting the ANN Model to the Training Set</p><p style='margin: 0'>In this video, you will learn how to fit the ANN model into the training set.</p></td></tr><tr><td style='padding: 15px;'><p>17. Predicting the Test Set Results</p><p style='margin: 0'>In this video, you will learn how to predict the test set results.</p></td></tr></tbody></table><br><h4>9. Convolutional Neural Networks (CNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to the learning goals for this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Components of Convolutional Neural Networks</p><p style='margin: 0'>In this video, you will learn about the components of Convolutional Neural Networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution Layer</p><p style='margin: 0'>In this video, you will learn about the Convolution layer.</p></td></tr><tr><td style='padding: 15px;'><p>4. Pooling Layer</p><p style='margin: 0'>In this video, you will learn about Pooling layer.</p></td></tr><tr><td style='padding: 15px;'><p>5. Fully Connected Layer</p><p style='margin: 0'>In this video, you will learn about fully connected layer.</p></td></tr></tbody></table><br><h4>10. Implementation of CNN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dataset</p><p style='margin: 0'>In this video, we are going to use the MNIST handwritten digit data which contains the images of handwritten digits.</p></td></tr><tr><td style='padding: 15px;'><p>2. Importing Libraries</p><p style='margin: 0'>In this video, you will learn how to import libraries.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the CNN Model</p><p style='margin: 0'>In this video, you will learn how to build the Convolutional Neural Networks (CNN) model.</p></td></tr><tr><td style='padding: 15px;'><p>4. Accuracy of the Model</p><p style='margin: 0'>In this video, you will learn about the accuracy of the Convolutional Neural Networks (CNN) model.</p></td></tr></tbody></table>

This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras.
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence (AI) tasks. You'll start with an introduction to deep learning where you'll focus on the fundamentals of the deep learning theory and find out how to use deep learning in Python, before moving on to artificial neural networks (ANNs). You'll learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, you'll discover how to make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. The video also covers convolutional neural networks (CNNs) at length and goes through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, you'll wrap up CNN implementation in Python. By the end of this course, you'll be able to use the concepts of deep learning to build neural networks in Python like a professional.
What You Will Learn
Get to grips with the fundamentals of the deep learning theory
Discover how to use deep learning in Python
Find out how to use different frameworks in Python
Build artificial neural networks with TensorFlow and Keras
Explore the implementation of ANNs and CNNs in Python
Audience
This course is for both programming beginners and professionals who want to expand on their knowledge of deep learning, or professional mathematicians looking to learn how to analyze data programmatically. Prerequisites include basic mathematical skills and Python coding experience.
Approach
This fundamentals training is excellent for both newcomers and seasoned professionals. This is the best course for you if you're looking for one that starts with the fundamentals and progresses to more complex subjects. It does not teach anything but what is necessary to get started with deep learning. This is pretty much everything you need to get started with the topic, and it also helps to keep the course quite concise.
Key Features
Fundamentals course designed for both beginners and experts alike * Use different frameworks in Python to solve real-world problems using deep learning and AI * Make predictions using linear regression, polynomial regression, and multivariate regression
Github Repo
https://github.com/PacktPublishing/Python-for-Deep-Learning---Build-Neural-Networks-in-Python
About the Author
Meta Brains
Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!
Course Outline
1. Introduction to Deep Learning

1. Course Introduction
In this video, we will have a quick introduction to the course.
2. What is Deep Learning?
In this video, we will understand what deep learning is.
3. Why is Deep Learning Important?
In this video, we will understand the importance of deep learning.
4. Software and Frameworks
In this video, you will learn about software and frameworks.
2. Artificial Neural Networks (ANN)

1. Section Introduction
In this video, we will have a quick introduction to this section.
2. Anatomy and Function of Neurons
In this video, you will learn about the anatomy and function of neurons.
3. An Introduction to The Neural Network
In this video, we will have a quick introduction to the neural network.
4. Architecture of a Neural Network
In this video, you will learn about the architecture of a neural network.
3. Propagation of Information in ANNs

1. Feed-Forward and Back Propagation Networks
In this video, you will learn about feed-forward and back propagation networks.
2. Backpropagation in Neural Networks
In this video, you will learn about backpropagation in neural networks.
3. Minimizing the Cost Function Using Backpropagation
In this video, you will learn how to minimize the cost function using backpropagation.
4. Neural Network Architectures

1. Single Layer Perceptron (SLP) Model
In this video, you will learn about the Single-layer perceptron (SLP) model.
2. Radial Basis Network (RBN)
In this video, you will learn about Radial Basis Network (RBN).
3. Multi-Layer Perceptron (MLP) Neural Network
In this video, you will learn about multi-layer perceptron (MLP) Neural Network.
4. Recurrent Neural Network (RNN)
In this video, you will learn about Recurrent Neural Network (RNN).
5. Long Short-Term Memory (LSTM) Networks
In this video, you will learn about Long Short-Term Memory (LSTM) networks.
6. Hopfield Neural Network
In this video, you will learn about Hopfield neural network.
7. Boltzmann Machine Neural Network
In this video, you will learn about Boltzmann Machine Neural Network.
5. Activation Functions

1. What is the Activation Function?
In this video, we will understand what the Activation function is.
2. Important Terminologies
In this video, we will understand some important terminologies like differential function and monotonic function.
3. The Sigmoid Function
In this video, you will learn about the Sigmoid function.
4. Hyperbolic Tangent Function
In this video, you will learn about the Hyperbolic tangent function.
5. SoftMax Function
In this video, you will learn about the SoftMax function.
6. Rectified Linear Unit (ReLU) Function
In this video, you will learn about Rectified Linear Unit (ReLU) function.
7. Leaky Rectified Linear Unit function
In this video, you will learn about the Leaky Rectified Linear Unit function.
6. Gradient Descent Algorithm

1. What is Gradient Descent?
In this video, we will understand what Gradient Descent is.
2. What is Stochastic Gradient Descent?
In this video, we will understand what is Stochastic Gradient Descent?
3. Gradient Descent versus Stochastic Gradient Descent
In this video, you will learn about Gradient Decent versus Stochastic Gradient Descent.
7. Summary - Overview of Neural Networks

1. How do Artificial Neural Networks Work?
In this video, we will understand how artificial neural networks work.
2. Advantages of Neural Networks
In this video, you will learn about the advantages of neural networks.
3. Disadvantages of Neural Networks
In this video, you will learn about the disadvantages of neural networks.
4. Applications of Neural Networks
In this video, you will learn about the applications of neural networks.
8. Implementation of ANN in Python

1. Introduction
In this video, we will have a quick Introduction to this section.
2. Exploring the Dataset
In this video, you will learn how to explore the dataset.
3. Problem Statement
In this video, we will understand the problem statement.
4. Data Pre-Processing
In this video, you will learn how data pre-processing works.
5. Loading the Dataset
In this video, you will learn how to load the dataset.
6. Splitting the Dataset into Independent and Dependent Variables
In this video, you will learn how to split the dataset into independent and dependent variables.
7. Label Encoding Using Scikit-Learn
In this video, you will learn how to work with label encoding using scikit-learn
8. One-hot encoding using scikit-learn
In this video, we will learn how to work with One-hot encoding using scikit-learn.
9. Training and Test Sets: Splitting Data
In this video, we will be working on splitting the data with training and test sets.
10. Feature Scaling
In this video, you will learn about feature scaling.
11. Building the Artificial Neural Network
In this video, you will learn how to build the Artificial Neural Network
12. Adding the Input Layer and the First Hidden Layer
In this video, you will learn how to add the input layer and the first hidden layer.
13. Adding the Next Hidden Layer
In this video, you will learn how to add the next hidden layer.
14. Adding the Output Layer
In this video, you will learn how to add the output layer.
15. Compiling the Artificial Neural Network
In this video, you will learn how to compile the Artificial Neural Network.
16. Fitting the ANN Model to the Training Set
In this video, you will learn how to fit the ANN model into the training set.
17. Predicting the Test Set Results
In this video, you will learn how to predict the test set results.
9. Convolutional Neural Networks (CNN)

1. Introduction
In this video, we will have a quick Introduction to the learning goals for this section.
2. Components of Convolutional Neural Networks
In this video, you will learn about the components of Convolutional Neural Networks.
3. Convolution Layer
In this video, you will learn about the Convolution layer.
4. Pooling Layer
In this video, you will learn about Pooling layer.
5. Fully Connected Layer
In this video, you will learn about fully connected layer.
10. Implementation of CNN in Python

1. Dataset
In this video, we are going to use the MNIST handwritten digit data which contains the images of handwritten digits.
2. Importing Libraries
In this video, you will learn how to import libraries.
3. Building the CNN Model
In this video, you will learn how to build the Convolutional Neural Networks (CNN) model.
4. Accuracy of the Model
In this video, you will learn about the accuracy of the Convolutional Neural Networks (CNN) model.

This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras.

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