Section 1 : Welcome

 		Lecture 1 Introduction 3:53
Lecture 2 Outline 12:38
Lecture 3 Where to get the code 8:16

Section 2 : Google Colab

 		Lecture 4 Intro to Google Colab, how to use a GPU or TPU for free 12:22
Lecture 5 Tensorflow 2 7:45
Lecture 6 Uploading your own data to Google Colab 11:32
Lecture 7 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 8:44
Lecture 8 Anyone Can Succeed in this Course 11:46

Section 3 : Machine Learning and Neurons

 		Lecture 9 What is Machine Learning 14:16
Lecture 10 Code Preparation (Classification Theory) 15:49
Lecture 11 Beginner's Code Preamble 4:29
Lecture 12 Classification Notebook 8:30
Lecture 13 Code Preparation (Regression Theory) 7:7
Lecture 14 Regression Notebook 10:25
Lecture 15 The Neuron 9:48
Lecture 16 How does a model learn 10:44
Lecture 17 Making Predictions 6:35
Lecture 18 Saving and Loading a Model 4:18
Lecture 19 Suggestion Box 2:54

Section 4 : Feedforward Artificial Neural Networks

 		Lecture 20 Artificial Neural Networks Section Introduction 5:50
Lecture 21 Beginners Rejoice The Math in This Course is Optional 11:37
Lecture 22 Forward Propagation 9:30
Lecture 23 The Geometrical Picture
Lecture 24 Activation Functions 17:8
Lecture 25 Multiclass Classification 8:32
Lecture 26 How to Represent Images
Lecture 27 Code Preparation (ANN) 12:32
Lecture 28 ANN for Image Classification 8:26
Lecture 29 ANN for Regression 10:55

Section 5 : Convolutional Neural Networks

 		Lecture 30 What is Convolution (part 1) 16:28
Lecture 31 What is Convolution (part 2)
Lecture 32 What is Convolution (part 3) 6:32
Lecture 33 Convolution on Color Images 15:49
Lecture 34 CNN Architecture 20:48
Lecture 35 CNN Code Preparation 15:1
Lecture 36 CNN for Fashion MNIST 6:36
Lecture 37 CNN for CIFAR-10 4:18
Lecture 38 Data Augmentation 8:41
Lecture 39 Batch Normalization 5:4
Lecture 40 Improving CIFAR-10 Results 10:11

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

 		Lecture 41 Sequence Data 18:16
Lecture 42 Forecasting 10:25
Lecture 43 Autoregressive Linear Model for Time Series Prediction 11:52
Lecture 44 Proof that the Linear Model Works 4:2
Lecture 45 Recurrent Neural Networks 21:22
Lecture 46 RNN Code Preparation 5:40
Lecture 47 RNN for Time Series Prediction 11:0
Lecture 48 Paying Attention to Shapes 8:15
Lecture 49 GRU and LSTM (pt 1) 15:59
Lecture 50 GRU and LSTM (pt 2) 11:26
Lecture 51 A More Challenging Sequence 9:9
Lecture 52 Demo of the Long Distance Problem 19:17
Lecture 53 RNN for Image Classification (Theory) 4:32
Lecture 54 RNN for Image Classification (Code) 3:48
Lecture 55 Stock Return Predictions using LSTMs (pt 1) 11:53
Lecture 56 Stock Return Predictions using LSTMs (pt 2)
Lecture 57 Stock Return Predictions using LSTMs (pt 3) 11:49
Lecture 58 Other Ways to Forecast 5:5

Section 7 : Natural Language Processing (NLP)

 		Lecture 59 Embeddings 13:2
Lecture 60 Code Preparation (NLP) 13:7
Lecture 61 Text Preprocessing 5:20
Lecture 62 Text Classification with LSTMs 8:9
Lecture 63 CNNs for Text 7:59
Lecture 64 Text Classification with CNNs 6:1

Section 8 : Recommender Systems

 		Lecture 65 Recommender Systems with Deep Learning Theory 12:58
Lecture 66 Recommender Systems with Deep Learning Code 9:5

Section 9 : Transfer Learning for Computer Vision

 		Lecture 67 Transfer Learning Theory 8:2
Lecture 68 Some Pre-trained Models (VGG, ResNet, Inception, MobileNet) 5:31
Lecture 69 Large Datasets and Data Generators 6:53
Lecture 70 2 Approaches to Transfer Learning 4:42
Lecture 71 Transfer Learning Code (pt 1) 10:40
Lecture 72 Transfer Learning Code (pt 2) 8:2

Section 10 : GANs (Generative Adversarial Networks)

 		Lecture 73 GAN Theory 15:40
Lecture 74 GAN Code 12:0

Section 11 : Deep Reinforcement Learning (Theory)

 		Lecture 75 Deep Reinforcement Learning Section Introduction 6:25
Lecture 76 Elements of a Reinforcement Learning Problem 20:9
Lecture 77 States, Actions, Rewards, Policies 9:15
Lecture 78 Markov Decision Processes (MDPs) 9:57
Lecture 79 The Return 4:46
Lecture 80 Value Functions and the Bellman Equation 9:44
Lecture 81 What does it mean to “learn” 7:9
Lecture 82 Solving the Bellman Equation with Reinforcement Learning (pt 1) 9:38
Lecture 83 Solving the Bellman Equation with Reinforcement Learning (pt 2) 11:51
Lecture 84 Epsilon-Greedy 5:59
Lecture 85 Q-Learning 14:5
Lecture 86 Deep Q-Learning DQN (pt 1) 13:55
Lecture 87 Deep Q-Learning DQN (pt 2)
Lecture 88 How to Learn Reinforcement Learning 5:47

Section 12 : Stock Trading Project with Deep Reinforcement Learning

 		Lecture 89 Reinforcement Learning Stock Trader Introduction 5:4
Lecture 90 Data and Environment 12:13
Lecture 91 Replay Buffer 5:30
Lecture 92 Program Design and Layout 6:47
Lecture 93 Code pt 1 5:36
Lecture 94 Code pt 2 9:30
Lecture 95 Code pt 3 6:18
Lecture 96 Code pt 4 7:15
Lecture 97 Reinforcement Learning Stock Trader Discussion 3:27

Section 13 : Advanced Tensorflow Usage

 		Lecture 98 What is a Web Service (Tensorflow Serving pt 1) 5:45
Lecture 99 Tensorflow Serving pt 2 16:45
Lecture 100 Tensorflow Lite (TFLite) 8:19
Lecture 101 Why is Google the King of Distributed Computing 8:37
Lecture 102 Training with Distributed Strategies 6:50
Lecture 103 Using the TPU Pdf

Section 14 : Low-Level Tensorflow

 		Lecture 104 Differences Between Tensorflow 1 9:52
Lecture 105 Constants and Basic Computation 9:30
Lecture 106 Variables and Gradient Tape 12:49
Lecture 107 Build Your Own Custom Model 10:37

Section 15 : In-Depth Loss Functions

 		Lecture 108 Mean Squared Error 9:1
Lecture 109 Binary Cross Entropy 5:46
Lecture 110 Categorical Cross Entropy 7:56

Section 16 : In-Depth Gradient Descent

 		Lecture 111 Gradient Descent 7:43
Lecture 112 Stochastic Gradient Descent 4:27
Lecture 113 Momentum 5:58
Lecture 114 Variable and Adaptive Learning Rates 11:35
Lecture 115 Adam 11:9

Section 17 : Extras

 		Lecture 116 Links to TF2 Pdf

Section 18 : Setting up your Environment (FAQ by Student Request)

 		Lecture 117 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:24
Lecture 118 Windows-Focused Environment Setup 2018 20:14
Lecture 119 Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer 22:15

Section 19 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 		Lecture 120 How to Code Yourself (part 1) 15:49
Lecture 121 How to Code Yourself (part 2) 9:23
Lecture 122 Proof that using Jupyter Notebook is the same as not using it 12:25
Lecture 123 Is Theano Dead 9:57

Section 20 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 		Lecture 124 How to Succeed in this Course (Long Version) 10:17
Lecture 125 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 21:58
Lecture 126 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:14
Lecture 127 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

Section 21 : Appendix FAQ Finale

 		Lecture 128 What is the Appendix 2:41