The Complete Self-Driving Car Course – Applied Deep Learning

The Complete Self-Driving Car Course – Applied Deep Learning
The Complete Self-Driving Car Course – Applied Deep Learning

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 18 Hours | 9.22 GB
eLearning | Skill level: All Levels


Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python

Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, 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 only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today.

Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a “learn by doing” style to create this amazing course.

You’ll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen.

By the end of the course, you will have built a fully functional self-driving car fuelled 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:

  • Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.
  • Learn to train a Perceptron-based Neural Network to classify between binary classes.
  • Learn to train Convolutional Neural Networks to identify between 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 to drive on its own!

No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.

This course also comes with all the source code and friendly support in the Q&A area.

What you’ll learn

  • Learn to apply Computer Vision and Deep Learning techniques to build automotive-related algorithms
  • Understand, build and train Convolutional Neural Networks with Keras
  • Simulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer Vision
  • Train a Deep Learning Model that can identify between 43 different Traffic Signs
  • Learn to use essential Computer Vision techniques to identify lane lines on a road
  • Learn to build and train powerful Neural Networks with Keras
  • Understand Neural Networks at the most fundamental perceptron-based level
+ Table of Contents

Introduction
1 Why This Course

Installation
2 Overview
3 Anaconda Distribution – Mac
4 Anaconda Distribution – Windows
5 Text Editor
6 Outro

Python Crash Course (Optional)
7 Python Crash Course Part 1 – Data Types
8 Slicing
9 Membership Operators
10 Mutability
11 Mutability II
12 Common Functions & Methods
13 Tuples
14 Sets
15 Dictionaries
16 Compound Data Structures
17 Part 1 – Outro
18 Jupyter Notebooks
19 Part 2 – Control Flow
20 If, else
21 elif
22 Complex Comparisons
23 For Loops
24 For Loops II
25 While Loops
26 Break
27 Part 2 – Outro
28 Part 3 – Functions
29 Arithmetic Operations
30 Functions
31 Scope
32 Doc Strings
33 Lambda & Higher Order Functions
34 Part 3 – Outro
35 Variables
36 Numeric Data Types
37 String Data Types
38 Booleans
39 Methods
40 Lists

NumPy Crash Course (Optional)
41 Overview
42 Part 4 – Outro
43 Vector Addition – Arrays vs Lists
44 Multidimensional Arrays
45 One Dimensional Slicing
46 Reshaping
47 Multidimensional Slicing
48 Manipulating Array Shapes
49 Matrix Multiplication
50 Stacking

Computer Vision Finding Lane Lines
51 Overview
52 Hough Transform II
53 Optimizing
54 Resource for upcoming video
55 Finding Lanes on Video
56 Source Code
57 Part 5 – Conclusion
58 Image needed for the next lesson
59 Loading Image
60 Grayscale Conversion
61 Smoothening Image
62 Simple Edge Detection
63 Region of Interest
64 Binary Numbers & Bitwise and
65 Line Detection – Hough Transform

The Perceptron
66 Overview
67 Error Function
68 Sigmoid
69 Sigmoid Implementation (Code)
70 Source code
71 Cross Entropy
72 Cross Entropy (Code)
73 Source Code
74 Gradient Descent
75 Gradient Descent (Code)
76 Recap
77 Machine Learning
78 Source Code
79 Part 6 – Conclusion
80 Supervised Learning – Friendly Example
81 Classification
82 Linear Model
83 Perceptrons
84 Weights
85 Project – Initial Stages
86 Sample Code for Initial Stages

Keras
87 Overview
88 Intro to Keras
89 Starter Code
90 Keras Models
91 Keras – Predictions
92 Source Code
93 Part 7 – Outro

Deep Neural Networks
94 Overview
95 Non-Linear Boundaries
96 Architecture
97 Feedforward Process
98 Error Function
99 Backpropagation
100 Code Implementation
101 Source Code
102 Section 8 – Conclusion

Multiclass Classification
103 Overview
104 Softmax
105 Cross Entropy
106 Implementation
107 Source Code
108 Section 9 – Outro

MNIST Image Recognition
109 Overview
110 Section 10 – Outro
111 MNIST Dataset
112 Train & Test
113 Hyperparameters
114 Implementation Part 1
115 Implementation Part 2
116 Resource for upcoming video
117 Implementation Part 3
118 Final Source Code

Convolutional Neural Networks
119 Overview
120 Final Source Code
121 Section 11 – Conclusion
122 Convolutions & MNIST
123 Convolutional Layer
124 Convolutions II
125 Pooling
126 Fully Connected Layer
127 Starter Code
128 Code Implementation I
129 Code Implementation II

Classifying Road Symbols
130 Overview
131 Section 12 – Outro
132 Traffic Signs Starter Code
133 Preprocessing Images
134 leNet Implementation
135 Fine-tuning Model
136 Resources Needed for Testing
137 Testing
138 Fit Generator
139 Final Source Code

Polynomial Regression
140 Overview
141 Implementation
142 Final Source Code
143 Section 13 – Conclusion

Behavioural Cloning
144 Overview
145 Collecting Data
146 Downloading Data
147 Balancing Data
148 Training & Validation Split
149 Preprocessing Images
150 Defining Nvidia Model
151 Drive.py code
152 Flask & Socket.io
153 Self Driving Car – Test 1
154 Generator – Augmentation Techniques
155 Batch Generator
156 Fit Generator
157 Final Source Code
158 Outro