Deep Learning: Advanced Computer Vision
4.7 (1,159 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
9,194 students enrolled

Deep Learning: Advanced Computer Vision

Advanced Computer Vision and Convolutional Neural Networks in Tensorflow, Keras, and Python
Highest Rated
4.7 (1,159 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
9,194 students enrolled
Last updated 11/2018
English
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Current price: $10.99 Original price: $199.99 Discount: 94% off
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This course includes
  • 7 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Requirements
  • Know how to build, train, and use a CNN using some library (preferably in Python)
  • Understand basic theoretical concepts behind convolution and neural networks
  • Decent Python coding skills, preferably in data science and the Numpy Stack
Description

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!


AWESOME FACTS:

  • One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.

  • Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math.

  • Another result? No complicated low-level code such as that written in TensorflowTheano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.



NOTES:

All the code for this course can be downloaded from my github:

/lazyprogrammer/machine_learning_examples

In the directory: cnn_class2

Make sure you always "git pull" so you have the latest version!


HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • Know how to build, train, and use a CNN using some library (preferably in Python)

  • Understand basic theoretical concepts behind convolution and neural networks

  • Decent Python coding skills, preferably in data science and the Numpy Stack

TIPS (for getting through the course):

  • Watch it at 2x.

  • Take handwritten notes. This will drastically increase your ability to retain the information.

  • Write down the equations. If you don't, I guarantee it will just look like gibberish.

  • Ask lots of questions on the discussion board. The more the better!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)


Who this course is for:
  • Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
  • Anyone who wants to learn about object detection algorithms like SSD and YOLO
  • Anyone who wants to learn how to write code for neural style transfer
  • Anyone who wants to use transfer learning
  • Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast
Course content
Expand all 67 lectures 07:12:41
+ Review
4 lectures 22:38
Review of CNNs
10:34
Where to get the code and data
02:26
Fashion MNIST
03:29
Review of CNNs in Code
06:09
+ VGG and Transfer Learning
9 lectures 41:20
VGG Section Intro
03:04
What's so special about VGG?
07:00
Transfer Learning
08:22
Relationship to Greedy Layer-Wise Pretraining
02:19
Getting the data
02:17
Code pt 1
09:23
Code pt 2
03:41
Code pt 3
03:27
VGG Section Summary
01:47
+ ResNet (and Inception)
15 lectures 01:01:29
ResNet Section Intro
02:49
ResNet Architecture
12:45
Building ResNet - Strategy
02:25
Building ResNet - Conv Block Details
03:34
Building ResNet - Conv Block Code
06:08
Building ResNet - Identity Block Details
01:23
Building ResNet - First Few Layers
02:27
Building ResNet - First Few Layers (Code)
04:15
Building ResNet - Putting it all together
04:19
Exercise: Apply ResNet
01:16
Applying ResNet
02:39
1x1 Convolutions
04:03
Optional: Inception
06:47
Different sized images using the same network
04:12
ResNet Section Summary
02:27
+ Object Detection (SSD)
10 lectures 53:51
Object Localization
06:36
What is Object Detection?
02:53
How would you find an object in an image?
08:40
The Problem of Scale
03:47
The Problem of Shape
03:52
SSD in Tensorflow
09:57
Modifying SSD to work on Video
05:04
Optional: Intersection over Union & Non-max Suppression
05:06
SSD Section Summary
02:52
+ Neural Style Transfer
7 lectures 43:27
Style Transfer Theory
11:23
Optimizing the Loss
08:02
Code pt 1
07:46
Code pt 2
07:13
Code pt 3
03:50
Style Transfer Section Summary
02:21
+ Bonus: Class Activation Maps
2 lectures 17:03
Class Activation Maps (Theory)
07:09
Class Activation Maps (Code)
09:54
+ Basics Review
5 lectures 35:01
(Review) Tensorflow Basics
07:27
(Review) Tensorflow Neural Network in Code
09:43
(Review) Keras Discussion
06:48
(Review) Keras Neural Network in Code
06:37
(Review) Keras Functional API
04:26
+ Appendix
12 lectures 02:25:15
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:30
How to Succeed in this Course (Long Version)
10:24
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Where to get discount coupons and FREE deep learning material
02:20
Python 2 vs Python 3
04:38
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07