Deep Learning: Advanced Computer Vision

Deep Learning: Advanced Computer Vision
Deep Learning: Advanced Computer Vision
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 1.29 GB

Advanced Computer Vision and Convolutional Neural Networks in Tensorflow, Keras, and Python

This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.

When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.

I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.

Let me give you a quick rundown of what this course is all about:

We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)

We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.

In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.

You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)

We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.

Another very popular computer vision task that makes use of CNNs is called neural style transfer.

This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.

I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!

What you’ll learn

  • Understand and apply transfer learning
  • Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception
  • Understand and use object detection algorithms like SSD
  • Understand and apply neural style transfer
  • Understand state-of-the-art computer vision topics
Table of Contents

Welcome
1 Introduction
2 Outline and Perspective
3 How to Succeed in this Course

Review
4 Review of CNNs
5 Where to get the code and data
6 Fashion MNIST
7 Review of CNNs in Code

VGG and Transfer Learning
8 VGG Section Intro
9 What’s so special about VGG
10 Transfer Learning
11 Relationship to Greedy Layer-Wise Pretraining
12 Getting the data
13 Code pt 1
14 Code pt 2
15 Code pt 3
16 VGG Section Summary

ResNet (and Inception)
17 ResNet Section Intro
18 Exercise Apply ResNet
19 Applying ResNet
20 x1 Convolutions
21 Optional Inception
22 Different sized images using the same network
23 ResNet Section Summary
24 ResNet Architecture
25 Building ResNet – Strategy
26 Building ResNet – Conv Block Details
27 Building ResNet – Conv Block Code
28 Building ResNet – Identity Block Details
29 Building ResNet – First Few Layers
30 Building ResNet – First Few Layers (Code)
31 Building ResNet – Putting it all together

Object Detection (SSD)
32 SSD Section Intro
33 SSD Section Summary
34 Object Localization
35 What is Object Detection
36 How would you find an object in an image
37 The Problem of Scale
38 The Problem of Shape
39 SSD in Tensorflow
40 Modifying SSD to work on Video
41 Optional Intersection over Union & Non-max Suppression

Neural Style Transfer
42 Style Transfer Section Intro
43 Style Transfer Theory
44 Optimizing the Loss
45 Code pt 1
46 Code pt 2
47 Code pt 3
48 Style Transfer Section Summary

Bonus Class Activation Maps
49 Class Activation Maps (Theory)
50 Class Activation Maps (Code)

Basics Review
51 (Review) Tensorflow Basics
52 (Review) Tensorflow Neural Network in Code
53 (Review) Keras Discussion
54 (Review) Keras Neural Network in Code
55 (Review) Keras Functional API

Appendix
56 What is the Appendix
57 Python 2 vs Python 3
58 What order should I take your courses in (part 1)
59 What order should I take your courses in (part 2)
60 Windows-Focused Environment Setup 2018
61 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
62 How to Succeed in this Course (Long Version)
63 How to Code by Yourself (part 1)
64 How to Code by Yourself (part 2)
65 Proof that using Jupyter Notebook is the same as not using it
66 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
67 Where to get discount coupons and FREE deep learning material