Section 1 : Introduction

lecture 1 Welcome to the Course! 1:11
lecture 2 BONUS Learning Paths Text
lecture 3 Some Additional Resources!! Text
lecture 4 This PDF resource will help you a lot! Pdf
lecture 5 5. FAQBot! Text
lecture 6 Get the materials Text

Section 2 : Module 1 Face Detection Intuition

lecture 7 Plan of attack 1:28
lecture 8 Your Shortcut To Becoming A Better Data Scientist! Text
lecture 9 Viola-Jones Algorithm 9:35
lecture 10 Haar-like Features 14:42
lecture 11 Integral Image 10:24
lecture 12 Training Classifiers 10:49
lecture 13 Adaptive Boosting (Adaboost)
lecture 14 Cascading

Section 3 : Module 1Face Detection with Open CV

lecture 15 Welcome to the Practical Applications 5:12
lecture 16 Installations Instructions (once and for all!) 14:41
lecture 17 Common Debug Tips Pdf
lecture 18 Face Detection - Step 1 6:49
lecture 19 Face Detection - Step 2 5:28
lecture 20 Face Detection - Step 3 3:53
lecture 21 Face Detection - Step 4 5:14
lecture 22 Face Detection - Step 5 4:53
lecture 23 Face Detection - Step 6 11:17

Section 4 : Homework Challenge Build a Happiness Detector

lecture 25 Homework Challenge - Solution (Video) 19:8
lecture 26 Homework Challenge - Solution (Code files) Zip

Section 5 : Module 2 Object DetectionIntuition

lecture 27 Plan of attack 2:9
lecture 28 How SSD is different 9:15
lecture 29 The Multi-Box Concept 10:19
lecture 30 Predicting Object Positions 9:53
lecture 31 The Scale Problem 12:43

Section 6 : Module 2Object Detection with SSD

lecture 32 Object Detection - Step 1 9:11
lecture 33 Object Detection - Step 2 5:11
lecture 34 Object Detection - Step 3 7:25
lecture 35 Object Detection - Step 4 8:59
lecture 36 Object Detection - Step 5 5:12
lecture 37 Object Detection - Step 6 17:49
lecture 38 Object Detection - Step 7 5:40
lecture 39 Object Detection - Step 8 3:49
lecture 40 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS Pdf
lecture 41 Object Detection - Step 10 16:44
lecture 42 Training the SSD Text

Section 7 : Homework Challenge Detect Epic Horses galloping in Monument

lecture 43 Homework Challenge - Instructions 0:10
lecture 44 Homework Challenge - Solution (Video) 15:1
lecture 45 Homework Challenge - Solution (Code files Zip

Section 8 : Module 3 Generative Adversarial Networks (GANs) Intuition

lecture 46 Plan of Attack 2:55
lecture 47 The Idea Behind GANs 6:58
lecture 48 How Do GANs Work- (Step 1)
lecture 49 How Do GANs Work- (Step 2) 5:2
lecture 50 How Do GANs Work- (Step 3) 4:23
lecture 51 Applications of GANs 12:51

Section 9 : Module 3 Image Creation with GANs

lecture 52 GANs - Step 1 9:35
lecture 53 GANs - Step 2
lecture 54 GANs - Step 3 4:55
lecture 55 GANs - Step 4 3:57
lecture 56 GANs - Step 5 19:17
lecture 57 GANs - Step 6 5:31
lecture 58 GANs - Step 7 2:34
lecture 59 GANs - Step 8 9:6
lecture 60 GANs - Step 9 20:28
lecture 61 GANs - Step 10 2:20
lecture 62 GANs - Step 11 6:15
lecture 63 GANs - Step 12 13:52
lecture 64 Special Thanks to Alexis Jacq 2:27
lecture 65 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS Pdf

Section 10 : Annex 1 Artificial Neural Networks

lecture 66 What is Deep Learning 12:34
lecture 67 Plan of Attack 2:52
lecture 68 The Neuron 16:15
lecture 69 The Activation Function 8:29
lecture 70 How do Neural Networks work 12:48
lecture 71 How do Neural Networks learn 12:59
lecture 72 Gradient Descent 10:13
lecture 73 Stochastic Gradient Descent 8:45
lecture 74 Backpropagation 5:22

Section 11 : Annex 2 Convolutional Neural Networks

lecture 75 Plan of Attack
lecture 76 What are convolutional neural networks 15:49
lecture 77 Step 1 - Convolution Operation 16:38
lecture 78 Step 1(b) - ReLU Layer 6:41
lecture 79 Step 2 - Pooling 14:13
lecture 80 Step 3 - Flattening 1:53
lecture 81 Step 4 - Full Connection 19:25
lecture 82 Summary 4:20
lecture 83 Softmax & Cross-Entropy 18:20