The Complete Self-Driving Car Course - Applied Deep Learning

Video description

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

About This Video

  • The transition from a beginner to deep learning expert
  • Learn through demonstrations as your instructor completes each task with you
  • No experience required

In Detail

Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world. This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You’ll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company.

This course will show you how to do the following:

  • Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
  • Train a perceptron-based neural network to classify between binary classes
  • Train convolutional neural networks to identify various traffic signs
  • Train deep neural networks to fit complex datasets
  • Master Keras, a power neural network library written in Python
  • Build and train a fully functional self-driving car

Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning. If you require support please email: customercare@packt.com

Table of contents

  1. Chapter 1 : Introduction
    1. Why This Course? 00:01:46
  2. Chapter 2 : Installation
    1. Overview 00:00:25
    2. Anaconda Distribution – Mac 00:02:37
    3. Anaconda Distribution – Windows 00:02:49
    4. Text Editor 00:02:47
    5. Outro 00:00:29
  3. Chapter 3 : Python Crash Course
    1. Python Crash Course Part 1 - Data Types 00:01:06
    2. Jupyter Notebooks 00:01:39
    3. Arithmetic Operations 00:04:24
    4. Variables 00:05:05
    5. Numeric Data Types 00:04:10
    6. String Data Types 00:05:46
    7. Booleans 00:04:27
    8. Methods 00:03:04
    9. Lists 00:05:32
    10. Slicing 00:08:16
    11. Membership Operators 00:02:51
    12. Mutability 00:04:09
    13. Mutability II 00:04:45
    14. Common Functions & Methods 00:07:32
    15. Tuples 00:03:32
    16. Sets 00:02:58
    17. Dictionaries 00:05:20
    18. Compound Data Structures 00:02:50
    19. Part 1 – Outro 00:00:15
    20. Part 2 - Control Flow 00:00:47
    21. If, else 00:04:47
    22. elif 00:06:53
    23. Complex Comparisons 00:05:11
    24. For Loops 00:07:18
    25. For Loops II 00:03:07
    26. While Loops 00:03:07
    27. Break 00:03:24
    28. Part 2 – Outro 00:00:17
    29. Part 3 – Functions 00:00:52
    30. Functions 00:05:35
    31. Scope 00:01:45
    32. Doc Strings 00:02:45
    33. Lambda & Higher Order Functions 00:06:08
    34. Part 3 – Outro 00:00:42
  4. Chapter 4 : NumPy Crash Course
    1. Overview 00:00:48
    2. Vector Addition - Arrays vs Lists 00:12:03
    3. Multidimensional Arrays 00:11:46
    4. One Dimensional Slicing 00:03:33
    5. Reshaping 00:03:35
    6. Multidimensional Slicing 00:07:21
    7. Manipulating Array Shapes 00:08:17
    8. Matrix Multiplication 00:04:19
    9. Stacking 00:14:00
    10. Part 4 – Outro 00:00:09
  5. Chapter 5 : Computer Vision: Finding Lane Lines
    1. Overview 00:00:36
    2. Loading Image 00:04:52
    3. Grayscale Conversion 00:04:32
    4. Smoothening Image 00:03:05
    5. Simple Edge Detection 00:04:21
    6. Region of Interest 00:07:42
    7. Binary Numbers & Bitwise_and 00:09:45
    8. Line Detection - Hough Transform 00:10:54
    9. Hough Transform II 00:13:26
    10. Optimizing 00:14:46
    11. Finding Lanes on Video 00:06:33
    12. Part 5 – Conclusion 00:00:34
  6. Chapter 6 : The Perceptron
    1. Overview 00:01:45
    2. Machine Learning 00:02:51
    3. Supervised Learning - Friendly Example 00:04:25
    4. Classification 00:07:48
    5. Linear Model 00:06:52
    6. Perceptrons 00:04:08
    7. Weights 00:02:03
    8. Project - Initial Stages 00:10:58
    9. Error Function 00:03:36
    10. Sigmoid 00:05:56
    11. Sigmoid Implementation (Code) 00:11:47
    12. Cross Entropy 00:05:38
    13. Cross Entropy (Code) 00:07:42
    14. Gradient Descent 00:03:14
    15. Gradient Descent (Code) 00:08:46
    16. Recap 00:01:54
    17. Part 6 – Conclusion 00:00:40
  7. Chapter 7 : Keras
    1. Overview 00:00:30
    2. Intro to Keras 00:02:05
    3. Keras Models 00:21:09
    4. Keras – Predictions 00:19:26
    5. Part 7 – Outro 00:00:21
  8. Chapter 8 : Deep Neural Networks
    1. Overview 00:00:52
    2. Non-Linear Boundaries 00:05:06
    3. Architecture 00:09:01
    4. Feedforward Process 00:07:46
    5. Error Function 00:04:10
    6. Backpropagation 00:05:13
    7. Code Implementation 00:26:02
    8. Conclusion 00:00:23
  9. Chapter 9 : Multiclass Classification
    1. Overview 00:00:36
    2. Softmax 00:11:51
    3. Cross Entropy 00:08:16
    4. Implementation 00:30:56
    5. Outro 00:00:18
  10. Chapter 10 : MNIST Image Recognition
    1. Overview 00:00:49
    2. MNIST Dataset 00:05:25
    3. Train & Test 00:13:29
    4. Hyperparameters 00:07:05
    5. Implementation Part 1 00:33:46
    6. Implementation Part 2 00:20:14
    7. Implementation Part 3 00:11:50
    8. Section 10 – Outro 00:00:25
  11. Chapter 11 : Convolutional Neural Networks
    1. Overview 00:00:45
    2. Convolutions & MNIST 00:06:44
    3. Convolutional Layer 00:18:12
    4. Convolutions II 00:08:07
    5. Pooling 00:14:11
    6. Fully Connected Layer 00:06:23
    7. Code Implementation I 00:31:03
    8. Code Implementation II 00:26:22
    9. Section 11 – Conclusion 00:00:17
  12. Chapter 12 : Classifying Road Symbols
    1. Overview 00:01:01
    2. Preprocessing Images 00:42:58
    3. leNet Implementation 00:20:12
    4. Fine-tuning Model 00:14:27
    5. Testing 00:06:15
    6. Fit Generator 00:23:51
    7. Section 12 – Outro 00:00:43
  13. Chapter 13 : Polynomial Regression
    1. Overview 00:00:30
    2. Implementation 00:15:22
    3. Section 13 – Conclusion 00:00:22
  14. Chapter 14 : Behavioural Cloning
    1. Overview 00:03:11
    2. Collecting Data 00:17:35
    3. Downloading Data 00:17:53
    4. Balancing Data 00:11:32
    5. Training & Validation Split 00:11:28
    6. Preprocessing Images 00:18:05
    7. Defining Nvidia Model 00:27:10
    8. Flask & Socket.io 00:17:33
    9. Self Driving Car - Test 1 00:16:31
    10. Generator - Augmentation Techniques 00:34:29
    11. Batch Generator 00:10:59
    12. Fit Generator 00:19:21
    13. Outro 00:00:46

Product information

  • Title: The Complete Self-Driving Car Course - Applied Deep Learning
  • Author(s): Rayan Slim, Jad Slim, Amer Sharaf, Sarmad Tanveer
  • Release date: April 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838829414