Module 1 Section 1: Introduction
Unit 1 Welcome to the course
Module 2 Module 1 - Face Detection Intuition
Unit 1 Plan of Attack
Unit 2 Viola-Jones Algorithm
Unit 3 Haar-like Features
Unit 4 Integral Image
Unit 5 Training Classifiers
Unit 6 Adaptive Boosting (Adaboost)
Unit 7 Cascading
Module 3 Module 1 - Face Detection with OpenCV
Unit 1 Welcome to the Practical Applications
Unit 2 Installations Instructions (once and for all!)
Unit 3 Face Detection - Step 1
Unit 4 Face Detection - Step 2
Unit 5 Face Detection - Step 4
Unit 6 Face Detection - Step 3
Unit 7 Face Detection - Step 5
Unit 8 Face Detection - Step 6
Module 4 Section 4: Homework Challenge - Build a Happiness Detector
Unit 1 Homework Challenge - Instructions
Unit 2 Homework Challenge - Solution (Video)
Unit 3 Homework Challenge - Solution (Code files)
Module 5 Module 2 - Object Detection Intuition
Unit 1 Plan of Attack
Unit 2 How SSD is different
Unit 3 The Multi-Box Concept
Unit 4 Predicting Object Positions
Unit 5 The Scale Problem
Module 6 Module 2 - Object Detection with SSD
Unit 1 Object Detection - Step 1
Unit 2 Object Detection - Step 2
Unit 3 Object Detection - Step 3
Unit 4 Object Detection - Step 4
Unit 5 Object Detection - Step 5
Unit 6 Object Detection - Step 6
Unit 7 Object Detection - Step 7
Unit 8 Object Detection - Step 8
Unit 9 Object Detection - Step 9
Unit 10 Object Detection - Step 10
Unit 11 Training the SSD
Module 7 Homework Challenge - Detect Epic Horses galloping in Monument Valley
Unit 1 Homework Challenge - Instructions
Unit 2 Homework Challenge - Solution (Video)
Unit 3 Homework Challenge - Solution (Code files)
Module 8 Module 3 - Generative Adversarial Networks (GANs) Intuition
Unit 1 Plan of Attack
Unit 2 The Idea Behind GANs
Unit 3 How Do GANs Work? (Step 1)
Unit 4 How Do GANs Work? (Step 2)
Unit 5 How Do GANs Work? (Step 3)
Unit 6 Application of GANs
Module 9 Module 3 - Image Creation with GANs
Unit 1 GANs - Step 1
Unit 2 GANs - Step 2
Unit 3 GANs - Step 3
Unit 4 GANs - Step 4
Unit 5 GANs - Step 5
Unit 6 GANs - Step 6
Unit 7 GANs - Step 7
Unit 8 GANs - Step 8
Unit 9 GANs - Step 9
Unit 10 GANs - Step 10
Unit 11 GANs - Step 11
Unit 12 GANs - Step 12
Module 10 Annex 1: Artificial Neural Networks
Unit 1 What is Deep Learning?
Unit 2 Plan of Attack
Unit 3 The Neuron
Unit 4 The Activation Function
Unit 5 How do Neural Networks work?
Unit 6 How do Neural Networks learn?
Unit 7 Gradient Descent
Unit 8 Stochastic Gradient Descent
Unit 9 Backpropagation
Module 11 Annex 2: Convolutional Neural Networks
Unit 1 Plan of Attack
Unit 2 What are convolutional neural networks?
Unit 3 Step 1 - Convolution Operation
Unit 4 Step 1(b) - ReLU Layer
Unit 5 Step 2 - Pooling
Unit 6 Step 3 - Flattening
Unit 7 Step 4 - Full Connection
Unit 8 Summary
Unit 9 Softmax & Cross-Entropy
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Computer Vision A-Z™: Learn OpenCV, GANs and Cutting Edge AI

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Course Description

*** AS SEEN ON KICKSTARTER ***

You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.

But what if you could also become a creator?

What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Sounds too good to be true, doesn't it?

But there actually is a way..

Computer Vision is by far the easiest way of becoming a creator.

And it's not only the easiest way, it's also the branch of AI where there is the most to create.

Why? You'll ask.

That's because Computer Vision is applied everywhere. From health to retail to entertainment – the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.

Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of.

So now that raises the question: how do you break into the World of Computer Vision?

Up until now, computer vision has for the most part been a maze. A growing maze.

As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost.

On top of that, not only do you need to know how to use it – you also need to know how it works to maximise the advantage of using Computer Vision.

To this problem we want to bring…

Computer Vision A-Z.

With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!

Can't wait to see you inside the class,

Kirill & Hadelin