Description

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

Want to become a good data scientist? Then this is the right course for you. This course has been designed by IIT professionals who have mastered mathematics and data science. We will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner. We will walk you step-by-step into the world of deep learning. Each video seeks to improve your understanding of the challenging field of Deep Learning from a beginner to an advanced level. We will cover artificial neural networks, feedforward networks, backpropagation, regularization, convolution neural networks, practical on CNN, two real-world projects for CNN, transfer learning, recurrent neural networks, advanced RNN, a case study on NLP (Natural Language Processing), generate automatic programming code, and build a solid foundation for Python and machine learning. We will be solving a few real-world projects during this course and their complete solutions are also provided so that students can easily implement what has been taught. By the end of the course, you will be able to use Python's deep learning algorithms in real life. All the codes and supporting files for this course are available at: https://github.com/packtpublishing/deep-learning-with-real-world-projects

What You Will Learn

Learn to use Matplotlib for data visualization
Learn to use Seaborn for statistical plots
Learn to use NumPy and Pandas for data analysis
Learn all the mathematics required to understand deep learning algorithms
Learn all statistical principles and become a deep learning expert
Learn end to end data science solutions

Audience

A beginner in Python or any other object-oriented programming language should find this course helpful. Those who are already working on analytics and machine learning models and looking to leverage deep learning technologies to improve their problem-solving capacity should benefit from this course. Working professionals looking for a career transition to data science roles will be able to upskill.

Approach

In this hands-on course, we will be covering complex theory, algorithms, and coding libraries in a simple way that can be easily grasped by any beginner. We will also be solving a few real-world projects during this course and their complete solutions are also provided so that students can easily implement what has been taught.

Key Features

A practical course with multiple real-life projects in deep learning * All advanced level deep learning algorithms and techniques like regularizations, dropout, and more * Implement deep learning algorithms along with mathematic intuitions

Github Repo

https://github.com/packtpublishing/deep-learning-with-real-world-projects

About the Author

Geekshub Pvt. Ltd.

Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.

Course Outline

1. Course Introduction

This section introduces the course, and you will learn the history of deep learning, perceptron, multi-level perceptrons, and so on.

1. Introduction

In this video, we will cover a quick section introduction.

2. History of Deep Learning

In this video, we will cover the history of deep learning.

3. Perceptrons

In this video, we will cover Perceptrons.

4. Multi-Level Perceptrons

In this video, we will cover multi-level Perceptrons.

5. Neural Network Playground

In this video, we will cover neural network playground.

6. Representations

In this video, we will cover representations.

7. Training Neural Network - Part 1

In this video, we will cover training the neural network - part 1.

8. Training Neural Network - Part 2

In this video, we will cover training the neural network - part 2.

9. Training Neural Network - Part 3

In this video, we will cover training the neural network - part 3.

10. Activation Functions

In this video, we will cover activation functions.

2. Artificial Neural Networks-Introduction

This section introduces artificial neural networks. You will learn deep learning, perceptron for classifiers, multi classifier, neural networks, and more.

1. Introduction

In this video, we will cover a quick section introduction.

2. Deep Learning

In this video, we will cover deep learning.

3. Understanding the Human Brain

In this video, we will explore the human brain in the context of deep learning

4. Perceptron

In this video, we will cover Perceptron.

5. Perceptron for Classifiers

In this video, we will cover Perceptron for classifiers.

6. Perceptron in Depth

In this video, we will cover Perceptron in depth.

7. Homogeneous Coordinate

In this video, we will cover homogeneous coordinate.

8. Example for Perceptron

In this video, we will cover an example for Perceptron.

9. Multi-Classifier

In this video, we will cover multi-classifier.

10. Neural Networks

In this video, we will cover neural networks.

11. Input Layer

In this video, we will cover the input layer.

12. Output Layer

In this video, we will cover the output layer.

13. Sigmoid Function

In this video, we will cover the Sigmoid function.

14. Understanding MNIST

In this video, we will cover understanding MNIST.

15. Assumptions in Neural Networks

In this video, we will cover assumptions in neural networks.

16. Training in Neural Networks

In this video, we will cover training in neural networks.

17. Understanding Notations

In this video, we will cover understanding notations.

18. Activation Functions

In this video, we will cover activation functions.

3. ANN - Feed Forward Network

This section is about the feed forward network. Learn about online offline mode, bidirectional RNN (Recurrent Neural Network), Pseudocode, and so on.

1. Introduction

In this video, we will cover a quick section introduction.

2. Online Offline Mode

In this video, we will cover online offline mode.

3. Bidirectional RNN

In this video, we will cover bidirectional RNN.

4. Understanding Dimensions

In this video, we will cover understanding dimensions.

5. Pseudocode

In this video, we will cover Pseudocode.

6. Pseudocode for Batch

In this video, we will cover Pseudocode for Batch.

7. Vectorized Methods

In this video, we will cover vectorized methods.

4. Backpropagation

This section is about backpropagation in detail.

1. Introduction

In this video, we will cover a quick section introduction.

2. Introducing Loss Function

In this video, we will get introduced to the loss function.

3. Backpropagation Training - Part 1

In this video, we will cover backpropagation training - part 1.

4. Backpropagation Training - Part 2

In this video, we will cover backpropagation training - part 2.

5. Backpropagation Training - Part 3

In this video, we will cover backpropagation training - part 3.

6. Backpropagation Training - Part 4

In this video, we will cover backpropagation training - part 4.

7. Backpropagation Training - Part 5

In this video, we will cover backpropagation training - part 5.

8. Sigmoid Function

In this video, we will cover the Sigmoid function.

9. Backpropagation Training - Part 6

In this video, we will cover backpropagation training - part 6.

10. Backpropagation Training - Part 7

In this video, we will cover backpropagation training - part 7.

11. Backpropagation Training - Part 8

In this video, we will cover backpropagation training - part 8.

12. Backpropagation Training - Part 9

In this video, we will cover backpropagation training - part 9.

13. Backpropagation Training - Part 10

In this video, we will cover backpropagation training - part 10.

14. Pseudocode

In this video, we will cover Pseudocode.

15. SGD

In this video, we will cover SGD.

16. Finding Global Minima

In this video, we will cover finding the global minima.

17. Training for Batches

In this video, we will cover training for batches.

5. Regularization

This section introduces regularization. This includes lectures on batch normalization, dropouts in parts, and more.

1. Introduction to Regularization

In this video, we will cover a quick introduction to regularization.

2. Dropouts Part 1

In this video, we will cover dropouts part 1.

3. Dropouts Part 2

In this video, we will cover dropouts part 2.

4. Batch Normalization - Part 1

In this video, we will cover batch normalization - part 1.

5. Batch Normalization - Part 2

In this video, we will cover batch normalization - part 2.

6. Batch Normalization - Part 3

In this video, we will cover batch normalization - part 3.

7. Introducing TensorFlow

In this video, we will cover a quick introduction to TensorFlow.

8. Introducing Keras

In this video, we will cover a quick introduction to Keras.

6. Convolution Neural Networks

This section is about convolution neural networks. This includes lectures on applications for CNN (Convolutional Neural Networks), images, stride and padding, and so on.

1. Introduction

In this video, we will cover a quick section introduction.

2. Applications for CNN

In this video, we will cover applications for CNN.

3. Idea Behind CNN - Part 1

In this video, we will cover the idea behind CNN - part 1.

4. Idea Behind CNN - Part 2

In this video, we will cover the idea behind CNN - part 2.

5. Images

In this video, we will study the structure of images and how we can feed it into the neural network

6. Video

In this video, we will understand how to perform video analysis using CNN network.

7. Convolution - Part 1

In this video, we will cover convolution - part 1.

8. Convolution - Part 2

In this video, we will cover convolution - part 2.

9. Stride and Padding

In this video, we will cover stride and padding.

10. Padding

In this video, we will cover padding.

11. Formulas

In this video, we will cover formulas.

12. Weight and Bias

In this video, we will cover weight and bias.

13. Feature Map

In this video, we will cover a feature map.

14. Pooling

In this video, we will cover pooling.

15. Combining Network

In this video, we will cover combining networks.

7. CNN-Keras

This section is about CNN-Keras. This includes case studies on CNN.

1. Introduction

In this video, we will cover a quick section introduction.

2. VGG16 (Visual Geometry Group)

In this video, we will cover VGG16 (Visual Geometry Group).

3. Practical on CNN: Case Study - Part 1

In this practical video, we will cover part 1 of our case study.

4. Practical on CNN: Case Study - Part 2

In this practical video, we will cover part 2 of our case study.

5. Practical on CNN: Case Study - Part 3

In this practical video, we will cover part 3 of our case study.

6. Practical on CNN: Case Study - Part 4

In this practical video, we will cover part 4 of our case study.

7. Practical on CNN: Case Study - Part 5

In this practical video, we will cover part 5 of our case study.

8. CNN-Transfer Learning

This section is about CNN-Transfer Learning. It includes lectures on AlexNet, GoogleNet, ResNet, and so on.

1. Introduction

In this video, we will cover a quick section introduction.

2. AlexNet

In this video, we will cover AlexNet.

3. GoogleNet

In this video, we will cover GoogleNet.

4. ResNet - Part 1

In this video, we will cover ResNet - part 1.

5. ResNet - Part 2

In this video, we will cover ResNet - part 2.

6. Transfer Learning - Part 1

In this video, we will cover transfer learning - part 1.

7. Transfer Learning - Part 2

In this video, we will cover transfer learning - part 2.

8. Transfer Learning - Part 3

In this video, we will cover transfer learning - part 3.

9. Transfer Learning - Part 4

In this video, we will cover transfer learning - part 4.

10. Transfer Learning - Part 5

In this video, we will cover transfer learning - part 5.

11. Transfer Learning - Part 6

In this video, we will cover transfer learning - part 6.

12. Case Study - Part 1

In this video, we will cover case study - part 1.

13. Case Study - Part 2

In this video, we will cover case study - part 2.

14. Case Study - Part 3

In this video, we will cover case study - part 3.

15. Analysis - Part 1

In this video, we will cover analysis - part 1.

16. Analysis - Part 2

In this video, we will cover analysis - part 2.

9. CNN-Industry Live Project: Playing with Real-World Natural Images

This section includes a live project of working with flower images.

1. Introduction

In this video, we will cover a quick section introduction.

2. Working with Flower Images: Case Study - Part 1

In this video, we will be working with flower images and cover part 1 of our case study.

3. Working with Flower Images: Case Study - Part 2

In this video, we will be working with flower images and cover part 2 of our case study.

4. Working with Flower Images: Case Study - Part 3

In this video, we will be working with flower images and cover part 3 of our case study.

5. Working with Flower Images: Case Study - Part 4

In this video, we will be working with flower images and cover part 4 of our case study.

6. Working with Flower Images: Case Study - Part 5

In this video, we will be working with flower images and cover part 5 of our case study.

7. Working with Flower Images: Case Study - Part 6

In this video, we will be working with flower images and cover part 6 of our case study.

8. Working with Flower Images: Case Study - Part 7

In this video, we will be working with flower images and cover part 7 of our case study.

9. Working with Flower Images: Case Study - Part 8

In this video, we will be working with flower images and cover part 8 of our case study.

10. Working with Flower Images: Case Study - Part 9

In this video, we will be working with flower images and cover part 9 of our case study.

11. Working with Flower Images: Case Study - Part 10

In this video, we will be working with flower images and cover part 10 of our case study.

12. Working with Flower Images: Case Study - Part 11

In this video, we will be working with flower images and cover part 11 of our case study.

13. Working with Flower Images: Case Study - Part 12

In this video, we will be working with flower images and cover part 12 of our case study.

14. Working with Flower Images: Case Study - Part 13

In this video, we will be working with flower images and cover part 13 of our case study.

15. Working with Flower Images: Case Study - Part 14

In this video, we will be working with flower images and cover part 14 of our case study.

10. CNN-Industry Live Project: Find Medical Abnormalities and Save a Life

This section includes a CNN-industry live project on working with X-Ray images.

1. Introduction

In this video, we will cover a quick section introduction.

2. Working with X-Ray images: Case Study - Part 1

In this video, we will be working with X-Ray images and cover part 1 of our case study.

3. Working with X-Ray images: Case Study - Part 2

In this video, we will be working with X-Ray images and cover part 2 of our case study.

4. Working with X-Ray images: Case Study - Part 3

In this video, we will be working with X-Ray images and cover part 3 of our case study.

5. Working with X-Ray images: Case Study - Part 4

In this video, we will be working with X-Ray images and cover part 4 of our case study.

6. Working with X-Ray images: Case Study - Part 5

In this video, we will be working with X-Ray images and cover part 5 of our case study.

7. Working with X-Ray images: Case Study - Part 6

In this video, we will be working with X-Ray images and cover part 6 of our case study.

11. Recurrent Neural Networks: Introduction

This section is about recurrent neural networks. It includes lectures on RNN formula, architecture, batch data, and so on.

1. Introduction to RNN

In this video, we will cover a quick introduction to RNN.

2. RNN - Part 1

In this video, we will cover RNN - part 1.

3. RNN - Part 2

In this video, we will cover RNN - part 2.

4. RNN Formula

In this video, we will cover RNN formulas.

5. Architecture

In this video, we will cover the architecture.

6. Batch data

In this video, we will cover batch data.

7. Simplified Notations

In this video, we will cover simplified notations.

8. Types of RNN - Part 1

In this video, we will cover types of RNN - part 1.

9. Types of RNN - Part 2

In this video, we will cover types of RNN - part 2.

10. Training RNN

In this video, we will learn about training RNN.

11. One-to-Many

In this video, we will cover one-to-many.

12. Vanishing Gradient

In this video, we will cover vanishing gradient.

12. Recurrent Neural Networks: LSTM

This section is about LSTM. It includes lectures on LSTM in parts, online offline mode, bidirectional RNN, and so on.

1. Introduction

In this video, we will cover a quick section introduction.

2. Online Offline Mode

In this video, we will cover online offline mode.

3. Bidirectional RNN

In this video, we will cover bidirectional RNN.

4. LSTM - Part 1

In this video, we will cover LSTM - part 1.

5. LSTM - Part 2

In this video, we will cover LSTM - part 2.

6. LSTM - Part 3

In this video, we will cover LSTM - part 3.

7. LSTM - Part 4

In this video, we will cover LSTM - part 4.

8. LSTM - Part 5

In this video, we will cover LSTM - part 5.

9. LSTM Equation

In this video, we will cover the LSTM equation.

10. Gated Recurrent Network (GRU)

In this video, we will cover Gated Recurrent Network (GRU).

13. Recurrent Neutral Networks: Part-Of-Speech Tagger

This section is part of speech tagger case studies.

1. Part-Of-Speech Tagger Case-Study (Part-1)

In this video, we will cover part-of-speech tagger case-study (part-1).

2. Part-Of-Speech Tagger Case- Study (Part-2)

In this video, we will cover part-of-speech tagger case-study (part-2).

3. Part-Of-Speech Tagger Case- Study (Part-3)

In this video, we will cover part-of-speech tagger case-study (part-3).

4. Part-Of-Speech Tagger Case- Study (Part-4)

In this video, we will cover part-of-speech tagger case-study (part-4).

5. Part-Of-Speech Tagger Case- Study (Part-5)

In this video, we will cover part-of-speech tagger case-study (part-5).

6. Part-Of-Speech Tagger Case- Study (Part-6)

In this video, we will cover part-of-speech tagger case-study (part-6).

7. Part-Of-Speech Tagger Case- Study (Part-7)

In this video, we will cover part-of-speech tagger case-study (part-7).

8. Part-Of-Speech Tagger Case- Study (Part-8)

In this video, we will cover part-of-speech tagger case-study (part-8).

9. Part-Of-Speech Tagger Case- Study (Part-9)

In this video, we will cover part-of-speech tagger case-study (part-9).

14. Text Generation Using RNN

This section is text generation using RNN.

1. Text Generation: Code Generator Case- Study (Part-1)

In this video, we will cover code generator case- study (part-1).

2. Text Generation: Code Generator Case- Study (Part-2)

In this video, we will cover code generator case- study (part-2).

3. Text Generation: Code Generator Case- Study (Part-3)

In this video, we will cover code generator case- study (part-3).

4. Text Generation: Code Generator Case- Study (Part-4)

In this video, we will cover code generator case- study (part-4).

15. Prerequisite - Python Fundamentals

In this pre-req section, we will cover Python fundamentals.

1. Installation of Python and Anaconda

In this video, we will cover the steps for the installation of Python and Anaconda.

2. Python Introduction

In this video, we will have a quick introduction to Python.

3. Variables in Python

In this video, you will learn about variables in Python.

4. Numeric Operations in Python

In this video, you will learn about numeric operations in Python.

5. Logical Operations

In this video, you will learn about logical operations.

6. If Else Loop

In this video, you will learn about the if else loop.

7. For While Loop

In this video, you will learn about the for while loop.

8. Functions

In this video, you will learn about functions.

9. Strings: Part 1

In this first part, we will work on strings.

10. Strings: Part 2

In this second part, we will continue with strings.

11. List: Part 1

In this first part, we will work on a list.

12. List: Part 2

In this second part, we will continue with the list.

13. List: Part 3

In this third part, we will continue with the list.

14. List: Part 4

In this 4th part, we will continue with the list.

15. Tuples

In this video, you will learn about tuples.

16. Sets

In this video, you will learn about sets.

17. Dictionaries

In this video, you will learn about dictionaries.

18. Comprehension

In this video, you will learn about comprehension.

16. Prerequisite - NumPy

In this prerequisite section, we will cover NumPy.

1. Introduction

In this video, we will have a quick introduction.

2. NumPy Operations: Part 1

In this first part, we will work on NumPy operations.

3. NumPy Operations: Part 2

In this second part, we will continue with NumPy operations.

17. Prerequisite - Pandas

In this prerequisite section, we will cover Pandas.

1. Introduction

In this video, we will have a quick introduction.

2. Series

In this video, you will learn about Series.

3. DataFrame

In this video, you will learn about DataFrame.

4. Operations: Part 1

In this first part, we will work on operations.

5. Operations: Part 2

In this second part, we will continue with operations.

6. Indexes

In this video, you will learn about indexes.

7. loc and iloc

In this video, you will learn about loc and iloc.

8. Reading CSV

In this video, you will learn how to read CSV.

9. Merging: Part 1

In this first part, we will work on merging.

10. groupby

In this video, you will learn about groupby.

11. Merging: Part 2

In this second part, we will continue with merging.

12. Pivot Tables

In this video, you will learn about pivot tables.

18. Prerequisite - Some Fun with Math

In this pre-req section, we will have some fun with math.

1. Linear Algebra: Vectors

In this video, you will learn about vectors.

2. Linear Algebra: Matrix: Part 1

In this first part, we will work on linear algebra and cover matrix.

3. Linear Algebra: Matrix: Part 2

In this second part, we will continue with matrix.

4. Linear Algebra: Going from 2D to nD: Part 1

In this first part, we will work on linear algebra and learn how to go from 2D to nD.

5. Linear Algebra: Going from 2D to nD: Part 2

In this second part, we will continue learning how to go from 2D to nD.

19. Prerequisite - Data Visualization

In this pre-req section, we will cover data visualization.

1. Matplotlib

In this video, you will learn about Matplotlib.

2. Seaborn

In this video, you will learn about Seaborn.

3. Case Study

In this video, we will work on a case study.

4. Seaborn on Time Series Data

In this video, you will learn about Seaborn on time series data.

20. Prerequisite - Simple Linear Regression

In this pre-req section, we will cover simple linear regression.

1. Introduction to Machine Learning

In this video, we will have a quick introduction to machine learning.

2. Types of Machine Learning

In this video, you will learn about types of machine learning.

3. Introduction to Linear Regression (LR)

In this video, we will have a quick introduction to linear regression (LR).

4. How LR Works?

In this video, we will understand how LR works.

5. Some Fun with Math Behind LR

In this video, we will have some fun with math behind LR.

6. R Square

In this video, you will learn about R Square.

7. LR Case Study: Part 1

In this video, we will work on a case study.

8. LR Case Study: Part 2

In this video, we will work on a case study.

9. LR Case Study: Part 3

In this video, we will work on a case study.

10. Residual Square Error (RSE)

In this video, you will learn about Residual Square Error (RSE).

21. Prerequisite - Gradient Descent

In this pre-req section, we will cover gradient descent.

1. Prerequisite for Gradient Descent: Part 1

In this first part, we will work on the prerequisite for gradient descent.

2. Prerequisite for Gradient Descent: Part 2

In this second part, we will continue with the prerequisite for gradient descent.

3. Cost Functions

In this video, you will learn about cost functions.

4. Defining Cost Functions More Formally

In this video, you will learn how to define cost functions more formally.

5. Gradient Descent

In this video, you will learn about gradient descent.

6. Optimization

In this video, you will learn about optimization.

7. Closed Form Versus Gradient Descent

In this video, you will learn about closed form versus gradient descent.

8. Gradient Descent Case Study

In this video, we will work on a case study.

22. Prerequisite - Classification: KNN

In this pre-req section, we will cover classification with KNN.

1. Introduction to Classification

In this video, we will have a quick introduction to classification.

2. Defining Classification Mathematically

In this video, you will learn how to define classification mathematically.

3. Introduction to KNN

In this video, we will have a quick introduction to KNN.

4. Accuracy of KNN

In this video, you will learn about the accuracy of KNN.

5. Effectiveness of KNN

In this video, you will learn about the effectiveness of KNN.

6. Distance Metrics

In this video, you will learn about distance metrics.

7. Distance Metrics: Part 2

In this second part, we will continue with distance metrics.

8. Finding k

In this video, you will learn how to find k.

9. KNN on Regression

In this video, you will learn about KNN on regression.

10. Case Study

In this video, we will work on a case study.

11. Classification Case 1

In this video, we will work on the classification case 1.

12. Classification Case 2

In this video, we will work on the classification case 2.

13. Classification Case 3

In this video, we will work on the classification case 3.

14. Classification Case 4

In this video, we will work on the classification case 4.

23. Prerequisite - Logistic Regression

In this prerequisite section, we will cover logistic regression.

1. Introduction

In this video, we will have a quick introduction.

2. Sigmoid Function

In this video, you will learn about the Sigmoid function.

3. Log Odds

In this video, you will learn about Log Odds.

4. Case Study

In this video, we will work on a case study.

24. Prerequisite - Advanced Machine Learning Algorithms

In this pre-req section, we will cover advanced machine learning algorithms.

1. Introduction

In this video, we will have a quick introduction.

2. Example: Part 1

In this first part, we will work on an example.

3. Example: Part 2

In this second part, we will continue with the example.

4. Optimal Solution

In this video, we will cover the optimal solution.

5. Case Study

In this video, we will work on a case study.

6. Regularization

In this video, you will learn about regularization.

7. Ridge and Lasso

In this video, you will learn about Ridge and Lasso.

8. Case Study

In this video, we will work on a case study.

9. Model Selection

In this video, you will learn about model selection.

10. Adjusted R Square

In this video, you will learn about Adjusted R Square.

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