Section 1 : Introduction

 		Lecture 1 Welcome 3:52
Lecture 2 Overview and Outline 13:3
Lecture 3 Where to get the Code 5:27

Section 2 : Google Colab

 		Lecture 4 Intro to Google Colab, how to use a GPU or TPU for free
Lecture 5 Uploading your own data to Google Colab 13:0
Lecture 6 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 8:22

Section 3 : Machine Learning and Neurons

 		Lecture 7 What is Machine Learning 14:15
Lecture 8 Regression Basics 14:28
Lecture 9 Regression Code Preparation 11:36
Lecture 10 Regression Notebook
Lecture 11 Moore's Law 6:47
Lecture 12 Moore's Law Notebook 13:41
Lecture 13 About Certification Pdf
Lecture 14 Linear Classification Basics 14:56
Lecture 15 Classification Code Preparation 6:42
Lecture 16 Classification Notebook 11:49
Lecture 17 About Proctor Testing Pdf
Lecture 18 Saving and Loading a Model 5:8
Lecture 19 A Short Neuroscience Primer 9:41
Lecture 20 How does a model learn 10:40
Lecture 21 Model With Logits 4:7
Lecture 22 Train Sets vs 10:2
Lecture 23 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 4 : Feedforward Artificial Neural Networks

 		Lecture 24 Artificial Neural Networks Section Introduction 5:49
Lecture 25 Forward Propagation 9:30
Lecture 26 The Geometrical Picture 9:34
Lecture 27 Activation Functions 17:8
Lecture 28 Multiclass Classification 9:28
Lecture 29 How to Represent Images 12:10
Lecture 30 Code Preparation (ANN) 14:46
Lecture 31 ANN for Image Classification 18:18
Lecture 32 ANN for Regression 10:43
Lecture 33 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 5 : Convolutional Neural Networks

 		Lecture 34 What is Convolution (part 1) 16:27
Lecture 35 What is Convolution (part 2) 5:46
Lecture 36 What is Convolution (part 3) 6:29
Lecture 37 Convolution on Color Images 15:58
Lecture 38 CNN Architecture 20:42
Lecture 39 CNN Code Preparation (part 1) 16:42
Lecture 40 CNN Code Preparation (part 2) 7:50
Lecture 41 CNN Code Preparation (part 3) 5:30
Lecture 42 CNN for Fashion MNIST 11:22
Lecture 43 CNN for CIFAR-10 7:55
Lecture 44 Data Augmentation 9:33
Lecture 45 Batch Normalization 5:4
Lecture 46 Improving CIFAR-10 Results 10:36
Lecture 47 About Certification Pdf

Section 6 : Recurrent Neural Networks, Time Series, and Sequence Data

 		Lecture 48 Sequence Data 22:2
Lecture 49 Forecasting 10:48
Lecture 50 Autoregressive Linear Model for Time Series 12:3
Lecture 51 Proof that the Linear Model Works 4:1
Lecture 52 Recurrent Neural Networks 21:21
Lecture 53 RNN Code Preparation 13:39
Lecture 54 RNN for Time Series Prediction 9:19
Lecture 55 Paying Attention to Shapes 9:24
Lecture 56 GRU and LSTM (pt 1) 15:59
Lecture 57 GRU and LSTM (pt 2) 11:35
Lecture 58 A More Challenging Sequence 10:19
Lecture 59 RNN for Image Classification (Theory) 4:32
Lecture 60 RNN for Image Classification (Code) 2:38
Lecture 61 Stock Return Predictions using LSTMs (pt 1) 12:13
Lecture 62 Stock Return Predictions using LSTMs (pt 2) 6:6
Lecture 63 Stock Return Predictions using LSTMs (pt 3) 11:35
Lecture 64 Other Ways to Forecast
Lecture 65 About Proctor Testing Pdf

Section 7 : Natural Language Processing (NLP)

 		Lecture 66 Embeddings 13:2
Lecture 67 Neural Networks with Embeddings 3:35
Lecture 68 Text Preprocessing (pt 1) 13:23
Lecture 69 About Certification Pdf
Lecture 70 Text Preprocessing (pt 2) 11:42
Lecture 71 Text Preprocessing (pt 3)
Lecture 72 Text Classification with LSTMs 8:45
Lecture 73 CNNs for Text 11:57
Lecture 74 Text Classification with CNNs 4:39
Lecture 75 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 76 VIP Making Predictions with a Trained NLP Model 7:27
Lecture 77 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 8 : Recommender Systems

 		Lecture 78 Recommender Systems with Deep Learning Theory 10:16
Lecture 79 Recommender Systems with Deep Learning Code Preparation 9:28
Lecture 80 Recommender Systems with Deep Learning Code (pt 1) 8:43
Lecture 81 Recommender Systems with Deep Learning Code (pt 2) 12:20
Lecture 82 VIP Making Predictions with a Trained Recommender Model 4:41
Lecture 83 About Proctor Testing Pdf

Section 9 : Transfer Learning for Computer Vision

 		Lecture 84 Transfer Learning Theory 8:2
Lecture 85 Some Pre-trained Models (VGG, ResNet, Inception, MobileNet) 3:55
Lecture 86 Large Datasets 7:1
Lecture 87 2 Approaches to Transfer Learning 4:41
Lecture 88 Transfer Learning Code (pt 1) 9:25
Lecture 89 Transfer Learning Code (pt 2) 7:30
Lecture 90 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 10 : GANs (Generative Adversarial Networks)

 		Lecture 91 GAN Theory 15:52
Lecture 92 GAN Code Preparation 6:8
Lecture 93 GAN Code 9:11
Lecture 94 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 11 : Deep Reinforcement Learning (Theory)

 		Lecture 95 Deep Reinforcement Learning Section Introduction 6:23
Lecture 96 Elements of a Reinforcement Learning Problem 20:8
Lecture 97 States, Actions, Rewards, Policies 9:15
Lecture 98 Markov Decision Processes (MDPs) 9:55
Lecture 99 The Return 4:46
Lecture 100 Value Functions and the Bellman Equation 9:43
Lecture 101 What does it mean to “learn” 7:5
Lecture 102 Solving the Bellman Equation with Reinforcement Learning (pt 1) 9:38
Lecture 103 Solving the Bellman Equation with Reinforcement Learning (pt 2) 11:51
Lecture 104 Epsilon-Greedy 5:56
Lecture 105 Q-Learning 14:4
Lecture 106 Deep Q-Learning DQN (pt 1) 13:55
Lecture 107 Deep Q-Learning DQN (pt 2) 10:10
Lecture 108 How to Learn Reinforcement Learning 5:47

Section 12 : Stock Trading Project with Deep Reinforcement Learning

 		Lecture 109 Reinforcement Learning Stock Trader Introduction 5:4
Lecture 110 Data and Environment 12:12
Lecture 111 Replay Buffer 5:30
Lecture 112 Program Design and Layout 6:46
Lecture 113 Code pt 1 9:11
Lecture 114 Code pt 2 9:30
Lecture 115 Code pt 3 6:43
Lecture 116 Code pt 4 7:14
Lecture 117 Reinforcement Learning Stock Trader Discussion 3:25
Lecture 118 About Certification Pdf

Section 13 : VIP Uncertainty Estimation

 		Lecture 119 Custom Loss and Estimating Prediction Uncertainty 9:26
Lecture 120 Estimating Prediction Uncertainty Code 7:2

Section 14 : VIP Facial Recognition

 		Lecture 121 Facial Recognition Section Introduction 3:29
Lecture 122 Siamese Networks 10:8
Lecture 123 Code Outline 4:55
Lecture 124 Loading in the data 5:42
Lecture 125 Splitting the data into train and test 4:17
Lecture 126 Converting the data into pairs 4:54
Lecture 127 Generating Generators 4:55
Lecture 128 Creating the model and loss 4:18
Lecture 129 Accuracy and imbalanced classes 7:38
Lecture 130 Facial Recognition Section Summary 3:22

Section 15 : In-Depth Loss Functions

 		Lecture 131 Mean Squared Error
Lecture 132 Binary Cross Entropy 5:47
Lecture 133 Categorical Cross Entropy 7:56

Section 16 : In-Depth Gradient Descent

 		Lecture 134 Gradient Descent 7:42
Lecture 135 Stochastic Gradient Descent 4:25
Lecture 136 Momentum 5:57
Lecture 137 Variable and Adaptive Learning Rates 11:34
Lecture 138 Adam 11:6
Lecture 139 About Proctor Testing Pdf

Section 17 : Extras

 		Lecture 140 Links To Colab Notebooks Text
Lecture 141 Links to VIP Notebooks Text

Section 18 : Setting up your Environment

 		Lecture 142 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Thean 17:30
Lecture 143 Windows-Focused Environment Setup 2018 20:12
Lecture 144 Installing NVIDIA GPU-Accelerated Deep Learning Libraries 22:15

Section 19 : Extra Help With Python Coding For Beginners

 		Lecture 145 How to Code Yourself (part 1) 15:47
Lecture 146 How to Code Yourself (part 2) 9:23
Lecture 147 Proof that using Jupyter Notebook is the same as not using 12:24

Section 20 : Effective Learning Startegies For Machine Learning

 		Lecture 148 How to Succeed in this Course (Long Version) 10:17
Lecture 149 Is this for Beginners or Experts Academic 21:57
Lecture 150 What order should I take your courses in (part 1) 11:12
Lecture 151 What order should I take your courses in (part 2) 16:7

Section 21 : Appendix FAQ

 		Lecture 152 What is the Appendix 2:41