#### Section 1 : Introduction

 Lecture 1 Why This Course 1:30 Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

#### Section 2 : Installation

 Lecture 3 Overview 0:25 Lecture 4 Anaconda Distribution - Mac 2:42 Lecture 5 Anaconda Distribution - Windows 2:54 Lecture 6 Text Editor 2:47 Lecture 7 Outro 0:29

#### Section 3 : Python Crash Course (Optional)

 Lecture 8 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 9 About Proctor Testing Pdf Lecture 10 Python Crash Course Part 1 - Data Types 1:6 Lecture 11 Jupyter Notebooks 1:38 Lecture 12 Arithmetic Operations 4:23 Lecture 13 Variables 5:4 Lecture 14 Numeric Data Types 4:8 Lecture 15 String Data Types 5:45 Lecture 16 Booleans 4:27 Lecture 17 Methods 3:4 Lecture 18 Lists 5:29 Lecture 19 Slicing Lecture 20 Membership Operators 2:49 Lecture 21 Mutability 4:8 Lecture 22 Mutability II 4:44 Lecture 23 Common Functions & Methods 7:30 Lecture 24 Tuples 3:31 Lecture 25 Sets 2:57 Lecture 26 Dictionaries 5:18 Lecture 27 Compound Data Structures 2:49 Lecture 28 Part 1 - Outro 0:14 Lecture 29 Part 2 - Control Flow 0:46 Lecture 30 If, else 4:46 Lecture 31 elif 6:51 Lecture 32 Complex Comparisons 5:10 Lecture 33 For Loops 7:16 Lecture 34 For Loops II 3:4 Lecture 35 While Loops 3:6 Lecture 36 Break 3:23 Lecture 37 Part 2 - Outro 0:16 Lecture 38 Part 3 - Functions 0:51 Lecture 39 Functions 5:34 Lecture 40 Scope Lecture 41 Doc Strings 2:44 Lecture 42 Lambda & Higher Order Functions 6:6 Lecture 43 Part 3 - Outro 0:41

#### Section 4 : NumPy Crash Course (Optional)

 Lecture 44 Overview 0:47 Lecture 45 Vector Addition - Arrays vs Lists 12:2 Lecture 46 Multidimensional Arrays 11:42 Lecture 47 One Dimensional Slicing 3:31 Lecture 48 Reshaping 3:34 Lecture 49 Multidimensional Slicing 7:19 Lecture 50 Manipulating Array Shapes 8:13 Lecture 51 Matrix Multiplication 4:18 Lecture 52 Stacking 13:57 Lecture 53 Part 4 - Outro 0:7

#### Section 5 : Computer Vision Finding Lane Lines

 Lecture 54 Overview 0:35 Lecture 55 Image needed for the next lesson Text Lecture 56 Loading Image 4:43 Lecture 57 About Proctor Testing Pdf Lecture 58 Grayscale Conversion 4:29 Lecture 59 Smoothening Image 3:3 Lecture 60 Simple Edge Detection 4:18 Lecture 61 Region of Interest 7:39 Lecture 62 Binary Numbers & Bitwise_and 9:43 Lecture 63 Line Detection - Hough Transform 10:50 Lecture 64 Hough Transform II 13:19 Lecture 65 Optimizing Lecture 66 Resource for upcoming video Text Lecture 67 Finding Lanes on Video 6:16 Lecture 68 About Certification Pdf Lecture 69 Source Code Text Lecture 70 Part 5 - Conclusion 0:33

#### Section 6 : The Perceptron

 Lecture 71 Overview 1:44 Lecture 72 Machine Learning 2:50 Lecture 73 Supervised Learning - Friendly Example 4:24 Lecture 74 Classification 7:47 Lecture 75 Linear Model Lecture 76 Perceptrons 4:6 Lecture 77 Weights 2:2 Lecture 78 Project - Initial Stages 10:53 Lecture 79 Sample Code for Initial Stages Text Lecture 80 Error Function 3:34 Lecture 81 Sigmoid 5:51 Lecture 82 Sigmoid Implementation (Code) 11:44 Lecture 83 Source code Text Lecture 84 Cross Entropy 5:38 Lecture 85 Cross Entropy (Code) 7:40 Lecture 86 Source Code Text Lecture 87 Gradient Descent 3:13 Lecture 88 Gradient Descent (Code) 8:44 Lecture 89 Recap 1:53 Lecture 90 Source Code Text Lecture 91 Part 6 - Conclusion 0:39

#### Section 7 : Keras

 Lecture 92 Overview 0:29 Lecture 93 Intro to Keras 2:4 Lecture 94 About Certification Pdf Lecture 95 About Proctor Testing Pdf Lecture 96 Starter Code Text Lecture 97 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 98 Keras Models 21:8 Lecture 99 Keras - Predictions 19:20 Lecture 100 Source Code Text Lecture 101 Part 7 - Outro 0:20

#### Section 8 : Deep Neural Networks

 Lecture 102 Overview 0:51 Lecture 103 Non-Linear Boundaries 5:4 Lecture 104 Architecture 8:59 Lecture 105 Feedforward Process 7:44 Lecture 106 Error Function 4:9 Lecture 107 Backpropagation 5:9 Lecture 108 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 109 Code Implementation 25:59 Lecture 110 Source Code Text Lecture 111 Section 8 - Conclusion 0:22

#### Section 9 : Multiclass Classification

 Lecture 112 Overview 0:35 Lecture 113 Softmax 11:50 Lecture 114 Cross Entropy 8:14 Lecture 115 Implementation 30:54 Lecture 116 Source Code Text Lecture 117 Section 9 - Outro 0:18

#### Section 10 : MNIST Image Recognition

 Lecture 118 Overview 0:48 Lecture 119 MNIST Dataset 5:25 Lecture 120 Train & Test 13:27 Lecture 121 Hyperparameters 7:4 Lecture 122 Implementation Part 1 33:45 Lecture 123 About Certification Pdf Lecture 124 Implementation Part 2 20:10 Lecture 125 Resource for upcoming video Text Lecture 126 Implementation Part 3 11:48 Lecture 127 Final Source Code Text Lecture 128 Section 10 - Outro 0:24

#### Section 11 : Convolutional Neural Networks

 Lecture 129 Overview 0:45 Lecture 130 Convolutions & MNIST 6:44 Lecture 131 Convolutional Layer 18:11 Lecture 132 Convolutions II 8:6 Lecture 133 Pooling 14:10 Lecture 134 Fully Connected Layer 6:22 Lecture 135 Starter Code Text Lecture 136 Code Implementation I 30:59 Lecture 137 Code Implementation II 26:19 Lecture 138 Final Source Code Text Lecture 139 Section 11 - Conclusion 0:16

#### Section 12 : Classifying Road Symbols

 Lecture 140 Overview 1:0 Lecture 141 Traffic Signs Starter Code Text Lecture 142 Preprocessing Images 42:58 Lecture 143 leNet Implementation 20:11 Lecture 144 Fine-tuning Model 14:27 Lecture 145 Resources Needed for Testing Text Lecture 146 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 147 Fit Generator 23:50 Lecture 148 Final Source Code Text Lecture 149 Section 12 - Outro 0:42

#### Section 13 : Polynomial Regression

 Lecture 150 Overview 0:29 Lecture 151 Implementation 15:22 Lecture 152 Final Source Code Text Lecture 153 Section 13 - Conclusion 0:22

#### Section 14 : Behavioural Cloning

 Lecture 154 Overview 3:11 Lecture 155 Collecting Data 17:45 Lecture 156 Downloading Data 17:52 Lecture 157 Balancing Data 11:31 Lecture 158 Training & Validation Split 11:26 Lecture 159 Preprocessing Images Lecture 160 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf Lecture 161 Defining Nvidia Model 27:9 Lecture 162 Drive Text Lecture 163 About Certification Pdf Lecture 164 Flask & Socket 17:33 Lecture 165 Self Driving Car - Test 1 16:30 Lecture 166 About Proctor Testing Pdf Lecture 167 Generator - Augmentation Techniques 34:28 Lecture 168 Batch Generator 10:58 Lecture 169 Fit Generator 19:17 Lecture 170 Final Source Code Text Lecture 171 Outro 0:54