Deep Learning and Computer Vision A-Z + AI & ChatGPT Prizes

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome Challenge!

Lecture 2: Welcome to the Course!

Lecture 3: Learning Paths

Lecture 4: Some Additional Resources!!

Lecture 5: This PDF resource will help you a lot!

Chapter 2: Module 1 – Face Detection Intuition

Lecture 1: Plan of attack

Lecture 2: Viola-Jones Algorithm

Lecture 3: Haar-like Features

Lecture 4: Integral Image

Lecture 5: Training Classifiers

Lecture 6: Adaptive Boosting (Adaboost)

Lecture 7: Cascading

Chapter 3: Module 1 – Face Detection with OpenCV

Lecture 1: Welcome to the Practical Applications

Lecture 2: Installations Instructions (once and for all!)

Lecture 3: Common Debug Tips

Lecture 4: Face Detection – Step 1

Lecture 5: Face Detection – Step 2

Lecture 6: Face Detection – Step 3

Lecture 7: Face Detection – Step 4

Lecture 8: Face Detection – Step 5

Lecture 9: Face Detection – Step 6

Chapter 4: Homework Challenge – Build a Happiness Detector

Lecture 1: Homework Challenge – Instructions

Lecture 2: Homework Challenge – Solution (Video)

Lecture 3: Homework Challenge – Solution (Code files)

Chapter 5: Module 2 – Object Detection Intuition

Lecture 1: Plan of attack

Lecture 2: How SSD is different

Lecture 3: The Multi-Box Concept

Lecture 4: Predicting Object Positions

Lecture 5: The Scale Problem

Chapter 6: Module 2 – Object Detection with SSD

Lecture 1: Object Detection – Step 1

Lecture 2: Object Detection – Step 2

Lecture 3: Object Detection – Step 3

Lecture 4: Object Detection – Step 4

Lecture 5: Object Detection – Step 5

Lecture 6: Object Detection – Step 6

Lecture 7: Object Detection – Step 7

Lecture 8: Object Detection – Step 8

Lecture 9: Object Detection – Step 9

Lecture 10: Object Detection – Step 10

Lecture 11: Training the SSD

Chapter 7: Homework Challenge – Detect Epic Horses galloping in Monument Valley

Lecture 1: Homework Challenge – Instructions

Lecture 2: Homework Challenge – Solution (Video)

Lecture 3: Homework Challenge – Solution (Code files)

Chapter 8: Module 3 – Generative Adversarial Networks (GANs) Intuition

Lecture 1: Plan of Attack

Lecture 2: The Idea Behind GANs

Lecture 3: How Do GANs Work? (Step 1)

Lecture 4: How Do GANs Work? (Step 2)

Lecture 5: How Do GANs Work? (Step 3)

Lecture 6: Applications of GANs

Chapter 9: Module 3 – Image Creation with GANs

Lecture 1: GANs – Step 1

Lecture 2: GANs – Step 2

Lecture 3: GANs – Step 3

Lecture 4: GANs – Step 4

Lecture 5: GANs – Step 5

Lecture 6: GANs – Step 6

Lecture 7: GANs – Step 7

Lecture 8: GANs – Step 8

Lecture 9: GANs – Step 9

Lecture 10: GANs – Step 10

Lecture 11: GANs – Step 11

Lecture 12: GANs – Step 12

Lecture 13: Special Thanks to Alexis Jacq

Lecture 14: THANK YOU Video

Chapter 10: Annex 1: Artificial Neural Networks

Lecture 1: What is Deep Learning?

Lecture 2: Plan of Attack

Lecture 3: The Neuron

Lecture 4: The Activation Function

Lecture 5: How do Neural Networks work?

Lecture 6: How do Neural Networks learn?

Lecture 7: Gradient Descent

Lecture 8: Stochastic Gradient Descent

Lecture 9: Backpropagation

Chapter 11: Annex 2: Convolutional Neural Networks

Lecture 1: Plan of Attack

Lecture 2: What are convolutional neural networks?

Lecture 3: Step 1 – Convolution Operation

Lecture 4: Step 1(b) – ReLU Layer

Lecture 5: Step 2 – Pooling

Lecture 6: Step 3 – Flattening

Lecture 7: Step 4 – Full Connection

Lecture 8: Summary

Lecture 9: Softmax & Cross-Entropy

Chapter 12: Congratulations!! Don't forget your Prize 🙂

Lecture 1: Huge Congrats for completing the challenge!