Section 1 : Welcome
Lecture 1 Introduction copy 00:02:35 Duration
Lecture 2 Outline and Perspective 00:06:49 Duration
Lecture 3 Where to get the code 00:08:27 Duration
Lecture 4 Anyone Can Succeed in this Course 00:11:55 Duration

Section 2 : Machine Learning Basics Review
Lecture 1 What is Machine Learning 00:14:26 Duration
Lecture 2 Code Preparation (Classification Theory) 00:15:59 Duration
Lecture 3 Beginner's Code Preamble 00:04:38 Duration
Lecture 4 Classification Notebook 00:08:40 Duration
Lecture 5 Code Preparation (Regression Theory) 00:07:19 Duration
Lecture 6 Regression Notebook 00:10:35 Duration
Lecture 7 The Neuron 00:09:58 Duration
Lecture 8 How does a model learn
Lecture 9 Making Predictions 00:06:45 Duration
Lecture 10 Saving and Loading a Model 00:04:28 Duration
Lecture 11 Suggestion Box 00:03:04 Duration

Section 3 : Artificial Neural Networks (ANN) Review
Lecture 1 Artificial Neural Networks Section Introduction 00:06:00 Duration
Lecture 2 Forward Propagation 00:09:40 Duration
Lecture 3 The Geometrical Picture 00:09:44 Duration
Lecture 4 Activation Functions 00:17:18 Duration
Lecture 5 Multiclass Classification 00:08:41 Duration
Lecture 6 How to Represent Images 00:12:37 Duration
Lecture 7 Code Preparation (ANN) 00:12:42 Duration
Lecture 8 ANN for Image Classification 00:08:37 Duration
Lecture 9 ANN for Regression 00:11:05 Duration

Section 4 : Convolutional Neural Networks (CNN) Review
Lecture 1 What is Convolution (part 1) 00:16:38 Duration
Lecture 2 What is Convolution (part 2) 00:05:57 Duration
Lecture 3 What is Convolution (part 3) 00:06:41 Duration
Lecture 4 Convolution on Color Images 00:15:59 Duration
Lecture 5 CNN Architecture 00:20:58 Duration
Lecture 6 CNN Code Preparation 00:15:13 Duration
Lecture 7 CNN for Fashion MNIST 00:06:46 Duration
Lecture 8 CNN for CIFAR-10 00:04:28 Duration
Lecture 9 Data Augmentation 00:08:51 Duration
Lecture 10 Batch Normalization 00:05:14 Duration
Lecture 11 Improving CIFAR-10 Results 00:10:22 Duration

Section 5 : VGG and Transfer Learning
Lecture 1 VGG Section Intro 00:03:05 Duration
Lecture 2 What's so special about VGG
Lecture 3 Transfer Learning 00:08:22 Duration
Lecture 4 Relationship to Greedy Layer-Wise Pretraining 00:02:19 Duration
Lecture 5 Getting the data 00:02:17 Duration
Lecture 6 Code pt 1 00:09:23 Duration
Lecture 7 Code pt 2 00:03:41 Duration
Lecture 8 Code pt 3 00:03:27 Duration
Lecture 9 VGG Section Summary 00:01:48 Duration

Section 6 : ResNet (and Inception)
Lecture 1 ResNet Section Intro 00:02:49 Duration
Lecture 2 ResNet Architecture 00:12:45 Duration
Lecture 3 Building ResNet - Strategy 00:02:25 Duration
Lecture 4 Uh-oh! What Happens if the Implementation Changes 00:05:17 Duration
Lecture 5 Building ResNet - Conv Block Details 00:03:34 Duration
Lecture 6 Building ResNet - Conv Block Code 00:06:08 Duration
Lecture 7 Building ResNet - Identity Block Details 00:01:23 Duration
Lecture 8 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 9 Building ResNet - First Few Layers (Code) 00:04:15 Duration
Lecture 10 Building ResNet - Putting it all together 00:04:20 Duration
Lecture 11 Exercise Apply ResNet 00:01:16 Duration
Lecture 12 Applying ResNet 00:02:39 Duration
Lecture 13 Convolutions 00:04:03 Duration
Lecture 14 Optional Inception 00:06:48 Duration
Lecture 15 Different sized images using the same network 00:04:13 Duration
Lecture 16 ResNet Section Summary 00:02:27 Duration

Section 7 : Object Detection (SSD RetinaNet)
Lecture 1 SSD Section Intro 00:05:04 Duration
Lecture 2 Object Localization 00:06:36 Duration
Lecture 3 What is Object Detection 00:02:53 Duration
Lecture 4 How would you find an object in an image 00:08:40 Duration
Lecture 5 The Problem of Scale 00:03:48 Duration
Lecture 6 The Problem of Shape 00:03:52 Duration
Lecture 7 More Fun and Excitement 00:05:45 Duration
Lecture 8 Using Pretrained RetinaNet 00:11:14 Duration
Lecture 9 RetinaNet with Custom Dataset (pt 1) 00:04:26 Duration
Lecture 10 RetinaNet with Custom Dataset (pt 2) 00:09:21 Duration
Lecture 11 RetinaNet with Custom Dataset (pt 3) 00:07:05 Duration
Lecture 12 Optional Intersection over Union & Non-max Suppression 00:05:07 Duration
Lecture 13 SSD Section Summary 00:02:53 Duration

Section 8 : Neural Style Transfer
Lecture 1 Style Transfer Section Intro 00:02:53 Duration
Lecture 2 Style Transfer Theory 00:11:23 Duration
Lecture 3 Optimizing the Loss 00:08:02 Duration
Lecture 4 Code pt 1 00:07:46 Duration
Lecture 5 Code pt 2 00:07:13 Duration
Lecture 6 Code pt 3 00:03:50 Duration
Lecture 7 Style Transfer Section Summary

Section 9 : Class Activation Maps
Lecture 1 Class Activation Maps (Theory) 00:07:09 Duration
Lecture 2 Class Activation Maps (Code) 00:09:54 Duration

Section 10 : GANs (Generative Adversarial Networks)
Lecture 1 GAN Theory 00:15:52 Duration
Lecture 2 GAN Colab Notebook
Lecture 3 GAN Code 00:12:10 Duration

Section 11 : Object Localization Project
Lecture 1 Localization Introduction and Outline 00:13:38 Duration
Lecture 2 Localization Code Outline (pt 1) 00:10:39 Duration
Lecture 3 Object Localization Colab Notebooks
Lecture 4 Localization Code (pt 1) 00:09:10 Duration
Lecture 5 Localization Code Outline (pt 2) 00:04:52 Duration
Lecture 6 Localization Code (pt 2) 00:11:03 Duration
Lecture 7 Localization Code Outline (pt 3) 00:03:19 Duration
Lecture 8 Localization Code (pt 3) 00:04:16 Duration
Lecture 9 Localization Code Outline (pt 4) 00:03:20 Duration
Lecture 10 Localization Code (pt 4) 00:02:06 Duration
Lecture 11 Localization Code Outline (pt 5) 00:07:43 Duration
Lecture 12 Localization Code (pt 5) 00:08:39 Duration
Lecture 13 Localization Code Outline (pt 6) 00:07:07 Duration
Lecture 14 Localization Code (pt 6) 00:07:37 Duration
Lecture 15 Localization Code Outline (pt 7) 00:04:58 Duration
Lecture 16 Localization Code (pt 7) 00:12:08 Duration

Section 12 : Keras and Tensorflow 2 Basics Review
Lecture 1 (Review) Tensorflow Basics 00:07:27 Duration
Lecture 2 (Review) Tensorflow Neural Network in Code
Lecture 3 (Review) Keras Discussion 00:06:49 Duration
Lecture 4 (Review) Keras Neural Network in Code 00:06:38 Duration
Lecture 5 (Review) Keras Functional API 00:04:26 Duration
Lecture 6 (Review) How to easily convert Keras into Tensorflow 2 00:01:49 Duration

Section 13 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:30 Duration

Section 14 : Extra Help With Python Coding for Beginners (FAQ by Student
Lecture 1 How to Code by Yourself (part 1) 00:15:54 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same 00:12:29 Duration
Lecture 4 Python vs Python 00:04:38 Duration
Lecture 5 How to Succeed in this Course 00:10:24 Duration

Section 15 : Effective Learning Strategies for Machine Learning (FAQ by S
Lecture 1 Is this for Beginners or Experts Academic or 00:22:04 Duration
Lecture 2 Machine Learning and AI Prerequisite Roadmap 00:11:19 Duration
Lecture 3 Machine Learning and AI Prerequisite Roadmap 00:16:07 Duration

Section 16 : Appendix FAQ Finale
Lecture 1 What is the Appendix
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM