Description

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.

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Python for Deep Learning - Build Neural Networks in Python

By Packt

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