Computer Vision A Z

Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.

Last updated 2022-01-10 | 4.3

- Have a toolbox of the most powerful Computer Vision models
- Understand the theory behind Computer Vision
- Master OpenCV

What you'll learn

Have a toolbox of the most powerful Computer Vision models
Understand the theory behind Computer Vision
Master OpenCV
Master Object Detection
Master Facial Recognition
Create powerful Computer Vision applications

* Requirements

* Only High School Maths
* Basic Python programming knowledge

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

Who this course is for:

  • Anyone interested in Computer Vision or Artificial Intelligence

Course content

12 sections • 91 lectures

Welcome to the Course! Preview 01:10

BONUS: Learning Paths Preview 00:33

Some Additional Resources!! Preview 00:14

This PDF resource will help you a lot! Preview 00:32

FAQ, Q&A and Bug Help!

FAQBot! Preview 01:29

Get the materials Preview 00:06

Your Shortcut To Becoming A Better Data Scientist! Preview 02:05

Plan of attack Preview 01:27

Updates on Udemy Reviews Preview 01:09

Viola-Jones Algorithm Preview 09:35

Haar-like Features Preview 14:42

Integral Image Preview 10:23

Training Classifiers Preview 10:49

Adaptive Boosting (Adaboost) Preview 16:26

Cascading Preview 06:13

Face Detection Intuition

Welcome to the Practical Applications Preview 05:12

Installations Instructions (once and for all!) Preview 14:40

Common Debug Tips Preview 00:13

Please see the following debug tips if you are running into any trouble installing PyTorch or to see other common bugs that might pop up. 

Face Detection - Step 1 Preview 06:49

Face Detection - Step 2 Preview 05:28

Face Detection - Step 3 Preview 03:53

Face Detection - Step 4 Preview 05:13

Face Detection - Step 5 Preview 04:53

Face Detection - Step 6 Preview 11:16

Face Detection with OpenCV

Homework Challenge - Instructions Preview 00:39

Homework Challenge - Solution (Video) Preview 19:07

Homework Challenge - Solution (Code files) Preview 00:04

Plan of attack Preview 02:08

How SSD is different Preview 09:14

The Multi-Box Concept Preview 10:18

Predicting Object Positions Preview 09:52

The Scale Problem Preview 12:42

Object Detection Intuition

Object Detection - Step 1 Preview 09:11

Object Detection - Step 2 Preview 05:11

Object Detection - Step 3 Preview 07:24

Object Detection - Step 4 Preview 08:59

Object Detection - Step 5 Preview 05:12

Object Detection - Step 6 Preview 17:49

Object Detection - Step 7 Preview 05:40

Object Detection - Step 8 Preview 03:49

Object Detection - Step 9 Preview 14:08

Object Detection - Step 10 Preview 16:43

Training the SSD Preview 00:16

Object Detection with SSD

Homework Challenge - Instructions Preview 00:15

Homework Challenge - Solution (Video) Preview 15:01

Homework Challenge - Solution (Code files) Preview 00:04

Plan of Attack Preview 02:55

The Idea Behind GANs Preview 06:57

How Do GANs Work? (Step 1) Preview 12:12

How Do GANs Work? (Step 2) Preview 05:01

How Do GANs Work? (Step 3) Preview 04:23

Applications of GANs Preview 12:51

Generative Adversarial Networks (GANs) Intuition

GANs - Step 1 Preview 09:35

GANs - Step 2 Preview 18:51

GANs - Step 3 Preview 04:54

GANs - Step 4 Preview 03:57

GANs - Step 5 Preview 19:17

GANs - Step 6 Preview 05:30

GANs - Step 7 Preview 02:34

GANs - Step 8 Preview 09:06

GANs - Step 9 Preview 20:28

GANs - Step 10 Preview 02:19

GANs - Step 11 Preview 06:15

GANs - Step 12 Preview 13:51

Image Creation with GANs

Special Thanks to Alexis Jacq Preview 02:27

THANK YOU bonus video Preview 02:40

What is Deep Learning? Preview 12:34

Plan of Attack Preview 02:51

The Neuron Preview 16:15

The Activation Function Preview 08:29

How do Neural Networks work? Preview 12:47

How do Neural Networks learn? Preview 12:58

Gradient Descent Preview 10:12

Stochastic Gradient Descent Preview 08:44

Backpropagation Preview 05:21

Plan of Attack Preview 03:31

What are convolutional neural networks? Preview 15:49

Step 1 - Convolution Operation Preview 16:38

Step 1(b) - ReLU Layer Preview 06:41

Step 2 - Pooling Preview 14:13

Step 3 - Flattening Preview 01:52

Step 4 - Full Connection Preview 19:24

Summary Preview 04:19

Softmax & Cross-Entropy Preview 18:20