Section 1 : Welcome

Lecture 1 Introduction copy 2:35
Lecture 2 Outline and Perspective 6:49
Lecture 3 Where to get the code 8:27
Lecture 4 Anyone Can Succeed in this Course 11:55

Section 2 : Machine Learning Basics Review

Lecture 5 What is Machine Learning 14:26
Lecture 6 Code Preparation (Classification Theory) 15:59
Lecture 7 Beginner's Code Preamble 4:38
Lecture 8 Classification Notebook 8:40
Lecture 9 Code Preparation (Regression Theory) 7:19
Lecture 10 Regression Notebook 10:35
Lecture 11 The Neuron 9:58
Lecture 12 How does a model learn
Lecture 13 Making Predictions 6:45
Lecture 14 Saving and Loading a Model 4:28
Lecture 15 Suggestion Box 3:4

Section 3 : Artificial Neural Networks (ANN) Review

Lecture 16 Artificial Neural Networks Section Introduction 6:0
Lecture 17 Forward Propagation 9:40
Lecture 18 The Geometrical Picture 9:44
Lecture 19 Activation Functions 17:18
Lecture 20 Multiclass Classification 8:41
Lecture 21 How to Represent Images 12:37
Lecture 22 Code Preparation (ANN) 12:42
Lecture 23 ANN for Image Classification 8:37
Lecture 24 ANN for Regression 11:5

Section 4 : Convolutional Neural Networks (CNN) Review

Lecture 25 What is Convolution (part 1) 16:38
Lecture 26 What is Convolution (part 2) 5:57
Lecture 27 What is Convolution (part 3) 6:41
Lecture 28 Convolution on Color Images 15:59
Lecture 29 CNN Architecture 20:58
Lecture 30 CNN Code Preparation 15:13
Lecture 31 CNN for Fashion MNIST 6:46
Lecture 32 CNN for CIFAR-10 4:28
Lecture 33 Data Augmentation 8:51
Lecture 34 Batch Normalization 5:14
Lecture 35 Improving CIFAR-10 Results 10:22

Section 5 : VGG and Transfer Learning

Lecture 36 VGG Section Intro 3:5
Lecture 37 What's so special about VGG
Lecture 38 Transfer Learning 8:22
Lecture 39 Relationship to Greedy Layer-Wise Pretraining 2:19
Lecture 40 Getting the data 2:17
Lecture 41 Code pt 1 9:23
Lecture 42 Code pt 2 3:41
Lecture 43 Code pt 3 3:27
Lecture 44 VGG Section Summary 1:48

Section 6 : ResNet (and Inception)

Lecture 45 ResNet Section Intro 2:49
Lecture 46 ResNet Architecture 12:45
Lecture 47 Building ResNet - Strategy 2:25
Lecture 48 Uh-oh! What Happens if the Implementation Changes 5:17
Lecture 49 Building ResNet - Conv Block Details 3:34
Lecture 50 Building ResNet - Conv Block Code 6:8
Lecture 51 Building ResNet - Identity Block Details 1:23
Lecture 52 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 53 Building ResNet - First Few Layers (Code) 4:15
Lecture 54 Building ResNet - Putting it all together 4:20
Lecture 55 Exercise Apply ResNet 1:16
Lecture 56 Applying ResNet 2:39
Lecture 57 Convolutions 4:3
Lecture 58 Optional Inception 6:48
Lecture 59 Different sized images using the same network 4:13
Lecture 60 ResNet Section Summary 2:27

Section 7 : Object Detection (SSD RetinaNet)

Lecture 61 SSD Section Intro 5:4
Lecture 62 Object Localization 6:36
Lecture 63 What is Object Detection 2:53
Lecture 64 How would you find an object in an image 8:40
Lecture 65 The Problem of Scale 3:48
Lecture 66 The Problem of Shape 3:52
Lecture 67 More Fun and Excitement 5:45
Lecture 68 Using Pretrained RetinaNet 11:14
Lecture 69 RetinaNet with Custom Dataset (pt 1) 4:26
Lecture 70 RetinaNet with Custom Dataset (pt 2) 9:21
Lecture 71 RetinaNet with Custom Dataset (pt 3) 7:5
Lecture 72 Optional Intersection over Union & Non-max Suppression 5:7
Lecture 73 SSD Section Summary 2:53

Section 8 : Neural Style Transfer

Lecture 74 Style Transfer Section Intro 2:53
Lecture 75 Style Transfer Theory 11:23
Lecture 76 Optimizing the Loss 8:2
Lecture 77 Code pt 1 7:46
Lecture 78 Code pt 2 7:13
Lecture 79 Code pt 3 3:50
Lecture 80 Style Transfer Section Summary

Section 9 : Class Activation Maps

Lecture 81 Class Activation Maps (Theory) 7:9
Lecture 82 Class Activation Maps (Code) 9:54

Section 10 : GANs (Generative Adversarial Networks)

Lecture 83 GAN Theory 15:52
Lecture 84 GAN Colab Notebook Text
Lecture 85 GAN Code 12:10

Section 11 : Object Localization Project

Lecture 86 Localization Introduction and Outline 13:38
Lecture 87 Localization Code Outline (pt 1) 10:39
Lecture 88 Object Localization Colab Notebooks Text
Lecture 89 Localization Code (pt 1) 9:10
Lecture 90 Localization Code Outline (pt 2) 4:52
Lecture 91 Localization Code (pt 2) 11:3
Lecture 92 Localization Code Outline (pt 3) 3:19
Lecture 93 Localization Code (pt 3) 4:16
Lecture 94 Localization Code Outline (pt 4) 3:20
Lecture 95 Localization Code (pt 4) 2:6
Lecture 96 Localization Code Outline (pt 5) 7:43
Lecture 97 Localization Code (pt 5) 8:39
Lecture 98 Localization Code Outline (pt 6) 7:7
Lecture 99 Localization Code (pt 6) 7:37
Lecture 100 Localization Code Outline (pt 7) 4:58
Lecture 101 Localization Code (pt 7) 12:8

Section 12 : Keras and Tensorflow 2 Basics Review

Lecture 102 (Review) Tensorflow Basics 7:27
Lecture 103 (Review) Tensorflow Neural Network in Code
Lecture 104 (Review) Keras Discussion 6:49
Lecture 105 (Review) Keras Neural Network in Code 6:38
Lecture 106 (Review) Keras Functional API 4:26
Lecture 107 (Review) How to easily convert Keras into Tensorflow 2 1:49

Section 13 : Setting Up Your Environment (FAQ by Student Request)

Lecture 108 Windows-Focused Environment Setup 2018 20:20
Lecture 109 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:30

Section 14 : Extra Help With Python Coding for Beginners (FAQ by Student

Lecture 110 How to Code by Yourself (part 1) 15:54
Lecture 111 How to Code by Yourself (part 2) 9:23
Lecture 112 Proof that using Jupyter Notebook is the same 12:29
Lecture 113 Python vs Python 4:38
Lecture 114 How to Succeed in this Course 10:24

Section 15 : Effective Learning Strategies for Machine Learning (FAQ by S

Lecture 115 Is this for Beginners or Experts Academic or 22:4
Lecture 116 Machine Learning and AI Prerequisite Roadmap 11:19
Lecture 117 Machine Learning and AI Prerequisite Roadmap 16:7

Section 16 : Appendix FAQ Finale

Lecture 118 What is the Appendix
Lecture 119 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf