Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 85 lectures (11h 5m) | 5.84 GB

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

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!

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
Table of Contents

Introduction
1 Welcome to the Course!
2 BONUS Learning Paths
3 Some Additional Resources!!
4 This PDF resource will help you a lot!
5 FAQBot!
6 Get the materials
7 Your Shortcut To Becoming A Better Data Scientist!

Module 1 – Face Detection Intuition
8 Plan of attack
9 Updates on Udemy Reviews
10 Viola-Jones Algorithm
11 Haar-like Features
12 Integral Image
13 Training Classifiers
14 Adaptive Boosting (Adaboost)
15 Cascading

Module 1 – Face Detection with OpenCV
16 Welcome to the Practical Applications
17 Installations Instructions (once and for all!)
18 Common Debug Tips
19 Face Detection – Step 1
20 Face Detection – Step 2
21 Face Detection – Step 3
22 Face Detection – Step 4
23 Face Detection – Step 5
24 Face Detection – Step 6

Homework Challenge – Build a Happiness Detector
25 Homework Challenge – Instructions
26 Homework Challenge – Solution (Video)
27 Homework Challenge – Solution (Code files)

Module 2 – Object Detection Intuition
28 Plan of attack
29 How SSD is different
30 The Multi-Box Concept
31 Predicting Object Positions
32 The Scale Problem

Module 2 – Object Detection with SSD
33 Object Detection – Step 1
34 Object Detection – Step 2
35 Object Detection – Step 3
36 Object Detection – Step 4
37 Object Detection – Step 5
38 Object Detection – Step 6
39 Object Detection – Step 7
40 Object Detection – Step 8
41 Object Detection – Step 9
42 Object Detection – Step 10
43 Training the SSD

Homework Challenge – Detect Epic Horses galloping in Monument Valley
44 Homework Challenge – Instructions
45 Homework Challenge – Solution (Video)
46 Homework Challenge – Solution (Code files)

Module 3 – Generative Adversarial Networks (GANs) Intuition
47 Plan of Attack
48 The Idea Behind GANs
49 How Do GANs Work (Step 1)
50 How Do GANs Work (Step 2)
51 How Do GANs Work (Step 3)
52 Applications of GANs

Module 3 – Image Creation with GANs
53 GANs – Step 1
54 GANs – Step 2
55 GANs – Step 3
56 GANs – Step 4
57 GANs – Step 5
58 GANs – Step 6
59 GANs – Step 7
60 GANs – Step 8
61 GANs – Step 9
62 GANs – Step 10
63 GANs – Step 11
64 GANs – Step 12
65 Special Thanks to Alexis Jacq
66 THANK YOU bonus video

Annex 1 Artificial Neural Networks
67 What is Deep Learning
68 Plan of Attack
69 The Neuron
70 The Activation Function
71 How do Neural Networks work
72 How do Neural Networks learn
73 Gradient Descent
74 Stochastic Gradient Descent
75 Backpropagation

Annex 2 Convolutional Neural Networks

Websites you may like
76 Plan of Attack
77 What are convolutional neural networks
78 Step 1 – Convolution Operation
79 Step 1(b) – ReLU Layer
80 Step 2 – Pooling
81 Step 3 – Flattening
82 Step 4 – Full Connection
83 Summary
84 Softmax & Cross-Entropy

Bonus Lectures
85 YOUR SPECIAL BONUS

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