Section 1 : Welcome
Lecture 1 Introduction 00:03:53 Duration
Lecture 2 Outline 00:12:38 Duration
Lecture 3 Where to get the code 00:08:16 Duration

Section 2 : Google Colab
Lecture 1 Intro to Google Colab, how to use a GPU or TPU for free 00:12:22 Duration
Lecture 2 Tensorflow 2 00:07:45 Duration
Lecture 3 Uploading your own data to Google Colab 00:11:32 Duration
Lecture 4 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 00:08:44 Duration
Lecture 5 Anyone Can Succeed in this Course 00:11:46 Duration

Section 3 : Machine Learning and Neurons
Lecture 1 What is Machine Learning 00:14:16 Duration
Lecture 2 Code Preparation (Classification Theory) 00:15:49 Duration
Lecture 3 Beginner's Code Preamble 00:04:29 Duration
Lecture 4 Classification Notebook 00:08:30 Duration
Lecture 5 Code Preparation (Regression Theory) 00:07:07 Duration
Lecture 6 Regression Notebook 00:10:25 Duration
Lecture 7 The Neuron 00:09:48 Duration
Lecture 8 How does a model learn 00:10:44 Duration
Lecture 9 Making Predictions 00:06:35 Duration
Lecture 10 Saving and Loading a Model 00:04:18 Duration
Lecture 11 Suggestion Box 00:02:54 Duration

Section 4 : Feedforward Artificial Neural Networks
Lecture 1 Artificial Neural Networks Section Introduction 00:05:50 Duration
Lecture 2 Beginners Rejoice The Math in This Course is Optional 00:11:37 Duration
Lecture 3 Forward Propagation 00:09:30 Duration
Lecture 4 The Geometrical Picture
Lecture 5 Activation Functions 00:17:08 Duration
Lecture 6 Multiclass Classification 00:08:32 Duration
Lecture 7 How to Represent Images
Lecture 8 Code Preparation (ANN) 00:12:32 Duration
Lecture 9 ANN for Image Classification 00:08:26 Duration
Lecture 10 ANN for Regression 00:10:55 Duration

Section 5 : Convolutional Neural Networks
Lecture 1 What is Convolution (part 1) 00:16:28 Duration
Lecture 2 What is Convolution (part 2)
Lecture 3 What is Convolution (part 3) 00:06:32 Duration
Lecture 4 Convolution on Color Images 00:15:49 Duration
Lecture 5 CNN Architecture 00:20:48 Duration
Lecture 6 CNN Code Preparation 00:15:01 Duration
Lecture 7 CNN for Fashion MNIST 00:06:36 Duration
Lecture 8 CNN for CIFAR-10 00:04:18 Duration
Lecture 9 Data Augmentation 00:08:41 Duration
Lecture 10 Batch Normalization 00:05:04 Duration
Lecture 11 Improving CIFAR-10 Results 00:10:11 Duration

Section 6 : Recurrent Neural Networks, Time Series, and Sequence Data
Lecture 1 Sequence Data 00:18:16 Duration
Lecture 2 Forecasting 00:10:25 Duration
Lecture 3 Autoregressive Linear Model for Time Series Prediction 00:11:52 Duration
Lecture 4 Proof that the Linear Model Works 00:04:02 Duration
Lecture 5 Recurrent Neural Networks 00:21:22 Duration
Lecture 6 RNN Code Preparation 00:05:40 Duration
Lecture 7 RNN for Time Series Prediction 00:11:00 Duration
Lecture 8 Paying Attention to Shapes 00:08:15 Duration
Lecture 9 GRU and LSTM (pt 1) 00:15:59 Duration
Lecture 10 GRU and LSTM (pt 2) 00:11:26 Duration
Lecture 11 A More Challenging Sequence 00:09:09 Duration
Lecture 12 Demo of the Long Distance Problem 00:19:17 Duration
Lecture 13 RNN for Image Classification (Theory) 00:04:32 Duration
Lecture 14 RNN for Image Classification (Code) 00:03:48 Duration
Lecture 15 Stock Return Predictions using LSTMs (pt 1) 00:11:53 Duration
Lecture 16 Stock Return Predictions using LSTMs (pt 2)
Lecture 17 Stock Return Predictions using LSTMs (pt 3) 00:11:49 Duration
Lecture 18 Other Ways to Forecast 00:05:05 Duration

Section 7 : Natural Language Processing (NLP)
Lecture 1 Embeddings 00:13:02 Duration
Lecture 2 Code Preparation (NLP) 00:13:07 Duration
Lecture 3 Text Preprocessing 00:05:20 Duration
Lecture 4 Text Classification with LSTMs 00:08:09 Duration
Lecture 5 CNNs for Text 00:07:59 Duration
Lecture 6 Text Classification with CNNs 00:06:01 Duration

Section 8 : Recommender Systems
Lecture 1 Recommender Systems with Deep Learning Theory 00:12:58 Duration
Lecture 2 Recommender Systems with Deep Learning Code 00:09:05 Duration

Section 9 : Transfer Learning for Computer Vision
Lecture 1 Transfer Learning Theory 00:08:02 Duration
Lecture 2 Some Pre-trained Models (VGG, ResNet, Inception, MobileNet) 00:05:31 Duration
Lecture 3 Large Datasets and Data Generators 00:06:53 Duration
Lecture 4 2 Approaches to Transfer Learning 00:04:42 Duration
Lecture 5 Transfer Learning Code (pt 1) 00:10:40 Duration
Lecture 6 Transfer Learning Code (pt 2) 00:08:02 Duration

Section 10 : GANs (Generative Adversarial Networks)
Lecture 1 GAN Theory 00:15:40 Duration
Lecture 2 GAN Code 00:12:00 Duration

Section 11 : Deep Reinforcement Learning (Theory)
Lecture 1 Deep Reinforcement Learning Section Introduction 00:06:25 Duration
Lecture 2 Elements of a Reinforcement Learning Problem 00:20:09 Duration
Lecture 3 States, Actions, Rewards, Policies 00:09:15 Duration
Lecture 4 Markov Decision Processes (MDPs) 00:09:57 Duration
Lecture 5 The Return 00:04:46 Duration
Lecture 6 Value Functions and the Bellman Equation 00:09:44 Duration
Lecture 7 What does it mean to “learn” 00:07:09 Duration
Lecture 8 Solving the Bellman Equation with Reinforcement Learning (pt 1) 00:09:38 Duration
Lecture 9 Solving the Bellman Equation with Reinforcement Learning (pt 2) 00:11:51 Duration
Lecture 10 Epsilon-Greedy 00:05:59 Duration
Lecture 11 Q-Learning 00:14:05 Duration
Lecture 12 Deep Q-Learning DQN (pt 1) 00:13:55 Duration
Lecture 13 Deep Q-Learning DQN (pt 2)
Lecture 14 How to Learn Reinforcement Learning 00:05:47 Duration

Section 12 : Stock Trading Project with Deep Reinforcement Learning
Lecture 1 Reinforcement Learning Stock Trader Introduction 00:05:04 Duration
Lecture 2 Data and Environment 00:12:13 Duration
Lecture 3 Replay Buffer 00:05:30 Duration
Lecture 4 Program Design and Layout 00:06:47 Duration
Lecture 5 Code pt 1 00:05:36 Duration
Lecture 6 Code pt 2 00:09:30 Duration
Lecture 7 Code pt 3 00:06:18 Duration
Lecture 8 Code pt 4 00:07:15 Duration
Lecture 9 Reinforcement Learning Stock Trader Discussion 00:03:27 Duration

Section 13 : Advanced Tensorflow Usage
Lecture 1 What is a Web Service (Tensorflow Serving pt 1) 00:05:45 Duration
Lecture 2 Tensorflow Serving pt 2 00:16:45 Duration
Lecture 3 Tensorflow Lite (TFLite) 00:08:19 Duration
Lecture 4 Why is Google the King of Distributed Computing 00:08:37 Duration
Lecture 5 Training with Distributed Strategies 00:06:50 Duration
Lecture 6 Using the TPU

Section 14 : Low-Level Tensorflow
Lecture 1 Differences Between Tensorflow 1 00:09:52 Duration
Lecture 2 Constants and Basic Computation 00:09:30 Duration
Lecture 3 Variables and Gradient Tape 00:12:49 Duration
Lecture 4 Build Your Own Custom Model 00:10:37 Duration

Section 15 : In-Depth Loss Functions
Lecture 1 Mean Squared Error 00:09:01 Duration
Lecture 2 Binary Cross Entropy 00:05:46 Duration
Lecture 3 Categorical Cross Entropy 00:07:56 Duration

Section 16 : In-Depth Gradient Descent
Lecture 1 Gradient Descent 00:07:43 Duration
Lecture 2 Stochastic Gradient Descent 00:04:27 Duration
Lecture 3 Momentum 00:05:58 Duration
Lecture 4 Variable and Adaptive Learning Rates 00:11:35 Duration
Lecture 5 Adam 00:11:09 Duration

Section 17 : Extras
Lecture 1 Links to TF2

Section 18 : Setting up your Environment (FAQ by Student Request)
Lecture 1 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:24 Duration
Lecture 2 Windows-Focused Environment Setup 2018 00:20:14 Duration
Lecture 3 Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer 00:22:15 Duration

Section 19 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Code Yourself (part 1) 00:15:49 Duration
Lecture 2 How to Code Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:25 Duration
Lecture 4 Is Theano Dead 00:09:57 Duration

Section 20 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced 00:21:58 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:14 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2) 00:16:07 Duration

Section 21 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:41 Duration