Section 1 : Introduction
Lecture 1 Welcome to the Course! 00:01:11 Duration
Lecture 2 BONUS Learning Paths
Lecture 3 Some Additional Resources!!
Lecture 4 This PDF resource will help you a lot!
Lecture 5 5. FAQBot!
Lecture 6 Get the materials

Section 2 : Module 1 Face Detection Intuition
Lecture 1 Plan of attack 00:01:28 Duration
Lecture 2 Your Shortcut To Becoming A Better Data Scientist!
Lecture 3 Viola-Jones Algorithm 00:09:35 Duration
Lecture 4 Haar-like Features 00:14:42 Duration
Lecture 5 Integral Image 00:10:24 Duration
Lecture 6 Training Classifiers 00:10:49 Duration
Lecture 7 Adaptive Boosting (Adaboost)
Lecture 8 Cascading

Section 3 : Module 1Face Detection with Open CV
Lecture 1 Welcome to the Practical Applications 00:05:12 Duration
Lecture 2 Installations Instructions (once and for all!) 00:14:41 Duration
Lecture 3 Common Debug Tips
Lecture 4 Face Detection - Step 1 00:06:49 Duration
Lecture 5 Face Detection - Step 2 00:05:28 Duration
Lecture 6 Face Detection - Step 3 00:03:53 Duration
Lecture 7 Face Detection - Step 4 00:05:14 Duration
Lecture 8 Face Detection - Step 5 00:04:53 Duration
Lecture 9 Face Detection - Step 6 00:11:17 Duration

Section 4 : Homework Challenge Build a Happiness Detector
Lecture 1 Homework Challenge - Solution (Video) 00:19:08 Duration
Lecture 2 Homework Challenge - Solution (Code files)

Section 5 : Module 2 Object DetectionIntuition
Lecture 1 Plan of attack 00:02:09 Duration
Lecture 2 How SSD is different 00:09:15 Duration
Lecture 3 The Multi-Box Concept 00:10:19 Duration
Lecture 4 Predicting Object Positions 00:09:53 Duration
Lecture 5 The Scale Problem 00:12:43 Duration

Section 6 : Module 2Object Detection with SSD
Lecture 1 Object Detection - Step 1 00:09:11 Duration
Lecture 2 Object Detection - Step 2 00:05:11 Duration
Lecture 3 Object Detection - Step 3 00:07:25 Duration
Lecture 4 Object Detection - Step 4 00:08:59 Duration
Lecture 5 Object Detection - Step 5 00:05:12 Duration
Lecture 6 Object Detection - Step 6 00:17:49 Duration
Lecture 7 Object Detection - Step 7 00:05:40 Duration
Lecture 8 Object Detection - Step 8 00:03:49 Duration
Lecture 9 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS
Lecture 10 Object Detection - Step 10 00:16:44 Duration
Lecture 11 Training the SSD

Section 7 : Homework Challenge Detect Epic Horses galloping in Monument
Lecture 1 Homework Challenge - Instructions 00:00:10 Duration
Lecture 2 Homework Challenge - Solution (Video) 00:15:01 Duration
Lecture 3 Homework Challenge - Solution (Code files

Section 8 : Module 3 Generative Adversarial Networks (GANs) Intuition
Lecture 1 Plan of Attack 00:02:55 Duration
Lecture 2 The Idea Behind GANs 00:06:58 Duration
Lecture 3 How Do GANs Work- (Step 1)
Lecture 4 How Do GANs Work- (Step 2) 00:05:02 Duration
Lecture 5 How Do GANs Work- (Step 3) 00:04:23 Duration
Lecture 6 Applications of GANs 00:12:51 Duration

Section 9 : Module 3 Image Creation with GANs
Lecture 1 GANs - Step 1 00:09:35 Duration
Lecture 2 GANs - Step 2
Lecture 3 GANs - Step 3 00:04:55 Duration
Lecture 4 GANs - Step 4 00:03:57 Duration
Lecture 5 GANs - Step 5 00:19:17 Duration
Lecture 6 GANs - Step 6 00:05:31 Duration
Lecture 7 GANs - Step 7 00:02:34 Duration
Lecture 8 GANs - Step 8 00:09:06 Duration
Lecture 9 GANs - Step 9 00:20:28 Duration
Lecture 10 GANs - Step 10 00:02:20 Duration
Lecture 11 GANs - Step 11 00:06:15 Duration
Lecture 12 GANs - Step 12 00:13:52 Duration
Lecture 13 Special Thanks to Alexis Jacq 00:02:27 Duration
Lecture 14 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS

Section 10 : Annex 1 Artificial Neural Networks
Lecture 1 What is Deep Learning 00:12:34 Duration
Lecture 2 Plan of Attack 00:02:52 Duration
Lecture 3 The Neuron 00:16:15 Duration
Lecture 4 The Activation Function 00:08:29 Duration
Lecture 5 How do Neural Networks work 00:12:48 Duration
Lecture 6 How do Neural Networks learn 00:12:59 Duration
Lecture 7 Gradient Descent 00:10:13 Duration
Lecture 8 Stochastic Gradient Descent 00:08:45 Duration
Lecture 9 Backpropagation 00:05:22 Duration

Section 11 : Annex 2 Convolutional Neural Networks
Lecture 1 Plan of Attack
Lecture 2 What are convolutional neural networks 00:15:49 Duration
Lecture 3 Step 1 - Convolution Operation 00:16:38 Duration
Lecture 4 Step 1(b) - ReLU Layer 00:06:41 Duration
Lecture 5 Step 2 - Pooling 00:14:13 Duration
Lecture 6 Step 3 - Flattening 00:01:53 Duration
Lecture 7 Step 4 - Full Connection 00:19:25 Duration
Lecture 8 Summary 00:04:20 Duration
Lecture 9 Softmax & Cross-Entropy 00:18:20 Duration