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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

Course Curriculum

1.1 Welcome to the course!
1.1 Welcome to the course 00:00:00
2.1 Plan of Attack
2.1 Plan of Attack 00:00:00
2.2 Viola-Jones Algorithm
2.2 Viola-Jones Algorithm 00:00:00
2.3 Haar-like Features
2.3 Haar-like Features 00:00:00
2.4 Integral Image
2.4 Integral Image 00:00:00
2.5 Training Classifiers
2.5 Training Classifiers 00:00:00
2.6 Adaptive Boosting (Adaboost)
2.6 Adaptive Boosting (Adaboost) 00:00:00
2.7 Cascading
2.7 Cascading 00:00:00
3.1 Welcome to the Practical Applications
3.1 Welcome to the Practical Applications 00:00:00
3.2 Installations Instructions (once and for all!)
3.2 Installations Instructions (once and for all!) 00:00:00
3.3 Face Detection - Step 1
3.3 Face Detection – Step 1 00:00:00
3.4 Face Detection - Step 2
3.4 Face Detection – Step 2 00:00:00
3.5 Face Detection - Step 3
3.5 Face Detection – Step 3 00:00:00
3.6 Face Detection - Step 4
3.6 Face Detection – Step 4 00:00:00
3.7 Face Detection - Step 5
3.7 Face Detection – Step 5 00:00:00
3.8 Face Detection - Step 6
3.8 Face Detection – Step 6 00:00:00
4.1 Homework Challenge - Instructions
4.1 Homework Challenge – Instructions 00:00:00
4.2 Homework Challenge - Solution (Video)
4.2 Homework Challenge – Solution (Video) 00:00:00
4.3 Homework Challenge - Solution (Code files)
4.3 Homework Challenge – Solution (Code files) 00:00:00
5.1 Plan of Attack
5.1 Plan of Attack 00:00:00
5.2 How SSD is different
5.2 How SSD is different 00:00:00
5.3 The Multi-Box Concept
5.3 The Multi-Box Concept 00:00:00
5.4 Predicting Object Positions
5.4 Predicting Object Positions 00:00:00
5.5 The Scale Problem
5.5 The Scale Problem 00:00:00
6.1 Object Detection - Step 1
6.1 Object Detection – Step 1 00:00:00
6.2 Object Detection - Step 2
6.2 Object Detection – Step 2 00:00:00
6.3 Object Detection - Step 3
6.3 Object Detection – Step 3 00:00:00
6.4 Object Detection - Step 4
6.4 Object Detection – Step 4 00:00:00
6.5 Object Detection - Step 5
6.5 Object Detection – Step 5 00:00:00
6.6 Object Detection - Step 6
6.6 Object Detection – Step 6 00:00:00
6.7 Object Detection - Step 7
6.7 Object Detection – Step 7 00:00:00
6.8 Object Detection - Step 8
6.8 Object Detection – Step 8 00:00:00
6.9 Object Detection - Step 9
6.9 Object Detection – Step 9 00:00:00
6.10 Object Detection - Step 10
6.10 Object Detection – Step 10 00:00:00
6.11 Training the SSD
6.11 Training the SSD 00:00:00
7.1 Homework Challenge - Instructions
7.1 Homework Challenge – Instructions 00:00:00
7.2 Homework Challenge - Solution (Video)
7.2 Homework Challenge – Solution (Video) 00:00:00
7.3 Homework Challenge - Solution (Code files)
7.3 Homework Challenge – Solution (Code files) 00:00:00
8.1 Plan of Attack
8.1 Plan of Attack 00:00:00
8.2 The Idea Behind GANs
8.2 The Idea Behind GANs 00:00:00
8.3 How Do GANs Work (Step 1)
8.3 How Do GANs Work (Step 1) 00:00:00
8.4 How Do GANs Work (Step 2)
8.4 How Do GANs Work (Step 2) 00:00:00
8.5 How Do GANs Work (Step 3)
8.5 How Do GANs Work (Step 3) 00:00:00
8.6 Application of GANs
8.6 Application of GANs 00:00:00
9.1 GANs - Step 1
9.1 GANs – Step 1 00:00:00
9.2 GANs - Step 2
9.2 GANs – Step 2 00:00:00
9.3 GANs - Step 3
9.3 GANs – Step 3 00:00:00
9.4 GANs - Step 4
9.4 GANs – Step 4 00:00:00
9.5 GANs - Step 5
9.5 GANs – Step 5 00:00:00
9.6 GANs - Step 6
9.6 GANs – Step 6 00:00:00
9.7 GANs - Step 7
9.7 GANs – Step 7 00:00:00
9.8 GANs - Step 8
9.8 GANs – Step 8 00:00:00
9.9 GANs - Step 9
9.9 GANs – Step 9 00:00:00
9.10 GANs - Step 10
9.10 GANs – Step 10 00:00:00
9.11 GANs - Step 11
9.11 GANs – Step 11 00:00:00
9.12 GANs - Step 12
9.12 GANs – Step 12 00:00:00
10.1 What is Deep Learning
10.1 What is Deep Learning 00:00:00
10.2 Plan of Attack
10.2 Plan of Attack 00:00:00
10.3 The Neuron
10.3 The Neuron 00:00:00
10.4 The Activation Function
10.4 The Activation Function 00:00:00
10.5 How do Neural Networks work
10.5 How do Neural Networks work 00:00:00
10.6 How do Neural Networks learn
10.6 How do Neural Networks learn 00:00:00
10.7 Gradient Descent
10.7 Gradient Descent 00:00:00
10.8 Stochastic Gradient Descent
10.8 Stochastic Gradient Descent 00:00:00
10.9 Backpropagation
10.9 Backpropagation 00:00:00
11.1 Plan of attack
11.1 Plan of attack 00:00:00
11.2 What are Convolutional Neural Networks
11.2 What are Convolutional Neural Networks 00:00:00
11.3 Step 1 - Convolution Operation
11.3 Step 1 – Convolution Operation 00:00:00
11.4 Step 1(b) - ReLU Layer
11.4 Step 1(b) – ReLU Layer 00:00:00
11.5 Step 2 - Pooling
11.5 Step 2 – Pooling 00:00:00
11.6 Step 3 - Flattening
11.6 Step 3 – Flattening 00:00:00
11.7 Step 4 - Full Connection
11.7 Step 4 – Full Connection 00:00:00
11.8 Summary
11.8 Summary 00:00:00
11.9 Softmax & Cross-Entropy
11.9 Softmax & Cross-Entropy 00:00:00

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