Section 1 : Introduction
Lecture 1 Welcome 00:03:52 Duration
Lecture 2 Overview and Outline 00:13:03 Duration
Lecture 3 Where to get the Code 00:05:27 Duration

Section 2 : Google Colab
Lecture 1 Intro to Google Colab, how to use a GPU or TPU for free
Lecture 2 Uploading your own data to Google Colab 00:13:00 Duration
Lecture 3 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn 00:08:22 Duration

Section 3 : Machine Learning and Neurons
Lecture 1 What is Machine Learning 00:14:15 Duration
Lecture 2 Regression Basics 00:14:28 Duration
Lecture 3 Regression Code Preparation 00:11:36 Duration
Lecture 4 Regression Notebook
Lecture 5 Moore's Law 00:06:47 Duration
Lecture 6 Moore's Law Notebook 00:13:41 Duration
Lecture 7 About Certification
Lecture 8 Linear Classification Basics 00:14:56 Duration
Lecture 9 Classification Code Preparation 00:06:42 Duration
Lecture 10 Classification Notebook 00:11:49 Duration
Lecture 11 About Proctor Testing
Lecture 12 Saving and Loading a Model 00:05:08 Duration
Lecture 13 A Short Neuroscience Primer 00:09:41 Duration
Lecture 14 How does a model learn 00:10:40 Duration
Lecture 15 Model With Logits 00:04:07 Duration
Lecture 16 Train Sets vs 00:10:02 Duration
Lecture 17 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 4 : Feedforward Artificial Neural Networks
Lecture 1 Artificial Neural Networks Section Introduction 00:05:49 Duration
Lecture 2 Forward Propagation 00:09:30 Duration
Lecture 3 The Geometrical Picture 00:09:34 Duration
Lecture 4 Activation Functions 00:17:08 Duration
Lecture 5 Multiclass Classification 00:09:28 Duration
Lecture 6 How to Represent Images 00:12:10 Duration
Lecture 7 Code Preparation (ANN) 00:14:46 Duration
Lecture 8 ANN for Image Classification 00:18:18 Duration
Lecture 9 ANN for Regression 00:10:43 Duration
Lecture 10 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 5 : Convolutional Neural Networks
Lecture 1 What is Convolution (part 1) 00:16:27 Duration
Lecture 2 What is Convolution (part 2) 00:05:46 Duration
Lecture 3 What is Convolution (part 3) 00:06:29 Duration
Lecture 4 Convolution on Color Images 00:15:58 Duration
Lecture 5 CNN Architecture 00:20:42 Duration
Lecture 6 CNN Code Preparation (part 1) 00:16:42 Duration
Lecture 7 CNN Code Preparation (part 2) 00:07:50 Duration
Lecture 8 CNN Code Preparation (part 3) 00:05:30 Duration
Lecture 9 CNN for Fashion MNIST 00:11:22 Duration
Lecture 10 CNN for CIFAR-10 00:07:55 Duration
Lecture 11 Data Augmentation 00:09:33 Duration
Lecture 12 Batch Normalization 00:05:04 Duration
Lecture 13 Improving CIFAR-10 Results 00:10:36 Duration
Lecture 14 About Certification

Section 6 : Recurrent Neural Networks, Time Series, and Sequence Data
Lecture 1 Sequence Data 00:22:02 Duration
Lecture 2 Forecasting 00:10:48 Duration
Lecture 3 Autoregressive Linear Model for Time Series 00:12:03 Duration
Lecture 4 Proof that the Linear Model Works 00:04:01 Duration
Lecture 5 Recurrent Neural Networks 00:21:21 Duration
Lecture 6 RNN Code Preparation 00:13:39 Duration
Lecture 7 RNN for Time Series Prediction 00:09:19 Duration
Lecture 8 Paying Attention to Shapes 00:09:24 Duration
Lecture 9 GRU and LSTM (pt 1) 00:15:59 Duration
Lecture 10 GRU and LSTM (pt 2) 00:11:35 Duration
Lecture 11 A More Challenging Sequence 00:10:19 Duration
Lecture 12 RNN for Image Classification (Theory) 00:04:32 Duration
Lecture 13 RNN for Image Classification (Code) 00:02:38 Duration
Lecture 14 Stock Return Predictions using LSTMs (pt 1) 00:12:13 Duration
Lecture 15 Stock Return Predictions using LSTMs (pt 2) 00:06:06 Duration
Lecture 16 Stock Return Predictions using LSTMs (pt 3) 00:11:35 Duration
Lecture 17 Other Ways to Forecast
Lecture 18 About Proctor Testing

Section 7 : Natural Language Processing (NLP)
Lecture 1 Embeddings 00:13:02 Duration
Lecture 2 Neural Networks with Embeddings 00:03:35 Duration
Lecture 3 Text Preprocessing (pt 1) 00:13:23 Duration
Lecture 4 About Certification
Lecture 5 Text Preprocessing (pt 2) 00:11:42 Duration
Lecture 6 Text Preprocessing (pt 3)
Lecture 7 Text Classification with LSTMs 00:08:45 Duration
Lecture 8 CNNs for Text 00:11:57 Duration
Lecture 9 Text Classification with CNNs 00:04:39 Duration
Lecture 10 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 11 VIP Making Predictions with a Trained NLP Model 00:07:27 Duration
Lecture 12 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 8 : Recommender Systems
Lecture 1 Recommender Systems with Deep Learning Theory 00:10:16 Duration
Lecture 2 Recommender Systems with Deep Learning Code Preparation 00:09:28 Duration
Lecture 3 Recommender Systems with Deep Learning Code (pt 1) 00:08:43 Duration
Lecture 4 Recommender Systems with Deep Learning Code (pt 2) 00:12:20 Duration
Lecture 5 VIP Making Predictions with a Trained Recommender Model 00:04:41 Duration
Lecture 6 About Proctor Testing

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) 00:03:55 Duration
Lecture 3 Large Datasets 00:07:01 Duration
Lecture 4 2 Approaches to Transfer Learning 00:04:41 Duration
Lecture 5 Transfer Learning Code (pt 1) 00:09:25 Duration
Lecture 6 Transfer Learning Code (pt 2) 00:07:30 Duration
Lecture 7 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 10 : GANs (Generative Adversarial Networks)
Lecture 1 GAN Theory 00:15:52 Duration
Lecture 2 GAN Code Preparation 00:06:08 Duration
Lecture 3 GAN Code 00:09:11 Duration
Lecture 4 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 11 : Deep Reinforcement Learning (Theory)
Lecture 1 Deep Reinforcement Learning Section Introduction 00:06:23 Duration
Lecture 2 Elements of a Reinforcement Learning Problem 00:20:08 Duration
Lecture 3 States, Actions, Rewards, Policies 00:09:15 Duration
Lecture 4 Markov Decision Processes (MDPs) 00:09:55 Duration
Lecture 5 The Return 00:04:46 Duration
Lecture 6 Value Functions and the Bellman Equation 00:09:43 Duration
Lecture 7 What does it mean to “learn” 00:07:05 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 00:05:56 Duration
Lecture 11 Q-Learning 00:14:04 Duration
Lecture 12 Deep Q-Learning DQN (pt 1) 00:13:55 Duration
Lecture 13 Deep Q-Learning DQN (pt 2) 00:10:10 Duration
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 00:12:12 Duration
Lecture 3 Replay Buffer 00:05:30 Duration
Lecture 4 Program Design and Layout 00:06:46 Duration
Lecture 5 Code pt 1 00:09:11 Duration
Lecture 6 Code pt 2 00:09:30 Duration
Lecture 7 Code pt 3 00:06:43 Duration
Lecture 8 Code pt 4 00:07:14 Duration
Lecture 9 Reinforcement Learning Stock Trader Discussion 00:03:25 Duration
Lecture 10 About Certification

Section 13 : VIP Uncertainty Estimation
Lecture 1 Custom Loss and Estimating Prediction Uncertainty 00:09:26 Duration
Lecture 2 Estimating Prediction Uncertainty Code 00:07:02 Duration

Section 14 : VIP Facial Recognition
Lecture 1 Facial Recognition Section Introduction 00:03:29 Duration
Lecture 2 Siamese Networks 00:10:08 Duration
Lecture 3 Code Outline 00:04:55 Duration
Lecture 4 Loading in the data 00:05:42 Duration
Lecture 5 Splitting the data into train and test 00:04:17 Duration
Lecture 6 Converting the data into pairs 00:04:54 Duration
Lecture 7 Generating Generators 00:04:55 Duration
Lecture 8 Creating the model and loss 00:04:18 Duration
Lecture 9 Accuracy and imbalanced classes 00:07:38 Duration
Lecture 10 Facial Recognition Section Summary 00:03:22 Duration

Section 15 : In-Depth Loss Functions
Lecture 1 Mean Squared Error
Lecture 2 Binary Cross Entropy 00:05:47 Duration
Lecture 3 Categorical Cross Entropy 00:07:56 Duration

Section 16 : In-Depth Gradient Descent
Lecture 1 Gradient Descent 00:07:42 Duration
Lecture 2 Stochastic Gradient Descent 00:04:25 Duration
Lecture 3 Momentum 00:05:57 Duration
Lecture 4 Variable and Adaptive Learning Rates 00:11:34 Duration
Lecture 5 Adam 00:11:06 Duration
Lecture 6 About Proctor Testing

Section 17 : Extras
Lecture 1 Links To Colab Notebooks
Lecture 2 Links to VIP Notebooks

Section 18 : Setting up your Environment
Lecture 1 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Thean 00:17:30 Duration
Lecture 2 Windows-Focused Environment Setup 2018 00:20:12 Duration
Lecture 3 Installing NVIDIA GPU-Accelerated Deep Learning Libraries 00:22:15 Duration

Section 19 : Extra Help With Python Coding For Beginners
Lecture 1 How to Code Yourself (part 1) 00:15:47 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 00:12:24 Duration

Section 20 : Effective Learning Startegies For Machine Learning
Lecture 1 How to Succeed in this Course (Long Version) 00:10:17 Duration
Lecture 2 Is this for Beginners or Experts Academic 00:21:57 Duration
Lecture 3 What order should I take your courses in (part 1) 00:11:12 Duration
Lecture 4 What order should I take your courses in (part 2) 00:16:07 Duration

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