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

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size4.4 GB
  • Uploaded Byescobar623
  • Downloads190
  • Last checkedMar. 31st '20
  • Date uploadedMar. 30th '20
  • Seeders 12
  • Leechers8
Your ISP and Government can track your torrent activity! Hide your IP with a VPN!

Infohash : 842DB165F2AE0038F4553D6FEC45D10A155838BE

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



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 will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.

Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.

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 Tensorflow, Theano, 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.

Suggested Prerequisites:

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.

Created by Lazy Programmer Inc.
Last updated 12/2019
English
English [Auto-generated]

Files:

[GigaCourse.com] Udemy - Deep Learning and Computer Vision A-Z™ OpenCV, SSD & GANs 1. Introduction
  • 1. Welcome to the Course!.mp4 (21.9 MB)
  • 1. Welcome to the Course!.srt (1.8 KB)
  • 2. BONUS Learning Paths.html (2.4 KB)
  • 3. Some Additional Resources!!.html (0.6 KB)
  • 4. This PDF resource will help you a lot!.html (0.7 KB)
  • 4.1 Computer_Vision FAQ.pdf.pdf (1.4 MB)
  • 5. FAQBot!.html (1.8 KB)
10. Annex 1 Artificial Neural Networks
  • 1. What is Deep Learning.mp4 (31.3 MB)
  • 1. What is Deep Learning.srt (18.1 KB)
  • 2. Plan of Attack.mp4 (4.8 MB)
  • 2. Plan of Attack.srt (4.0 KB)
  • 3. The Neuron.mp4 (29.6 MB)
  • 3. The Neuron.srt (24.9 KB)
  • 4. The Activation Function.mp4 (14.8 MB)
  • 4. The Activation Function.srt (12.0 KB)
  • 5. How do Neural Networks work.mp4 (23.5 MB)
  • 5. How do Neural Networks work.srt (19.1 KB)
  • 6. How do Neural Networks learn.mp4 (26.6 MB)
  • 6. How do Neural Networks learn.srt (18.9 KB)
  • 7. Gradient Descent.mp4 (18.5 MB)
  • 7. Gradient Descent.srt (14.0 KB)
  • 8. Stochastic Gradient Descent.mp4 (16.8 MB)
  • 8. Stochastic Gradient Descent.srt (12.1 KB)
  • 9. Backpropagation.mp4 (10.9 MB)
  • 9. Backpropagation.srt (7.1 KB)
11. Annex 2 Convolutional Neural Networks
  • 1. Plan of Attack.mp4 (5.9 MB)
  • 1. Plan of Attack.srt (5.2 KB)
  • 2. What are convolutional neural networks.mp4 (29.5 MB)
  • 2. What are convolutional neural networks.srt (22.1 KB)
  • 3. Step 1 - Convolution Operation.mp4 (31.0 MB)
  • 3. Step 1 - Convolution Operation.srt (23.2 KB)
  • 4. Step 1(b) - ReLU Layer.mp4 (14.1 MB)
  • 4. Step 1(b) - ReLU Layer.srt (9.2 KB)
  • 5. Step 2 - Pooling.mp4 (40.3 MB)
  • 5. Step 2 - Pooling.srt (21.0 KB)
  • 6. Step 3 - Flattening.mp4 (3.3 MB)
  • 6. Step 3 - Flattening.srt (2.5 KB)
  • 7. Step 4 - Full Connection.mp4 (42.8 MB)
  • 7. Step 4 - Full Connection.srt (28.6 KB)
  • 8. Summary.mp4 (7.9 MB)
  • 8. Summary.srt (6.0 KB)
  • 9. Softmax & Cross-Entropy.mp4 (33.2 MB)
  • 9. Softmax & Cross-Entropy.srt (25.3 KB)
12. Bonus Lectures
  • 1. YOUR SPECIAL BONUS.html (1.1 KB)
2. Module 1 - Face Detection Intuition
  • 1. Plan of attack.mp4 (2.3 MB)
  • 1. Plan of attack.srt (2.0 KB)
  • 2. Updates on Udemy Reviews.mp4 (43.6 MB)
  • 2. Updates on Udemy Reviews.srt (3.3 KB)
  • 3. Viola-Jones Algorithm.mp4 (18.6 MB)
  • 3. Viola-Jones Algorithm.srt (15.3 KB)
  • 4. Haar-like Features.mp4 (30.6 MB)
  • 4. Haar-like Features.srt (21.1 KB)
  • 5. Integral Image.mp4 (20.4 MB)
  • 5. Integral Image.srt (14.7 KB)
  • 6. Training Classifiers.mp4 (20.9 MB)
  • 6. Training Classifiers.srt (15.8 KB)
  • 7. Adaptive Boosting (Adaboost).mp4 (28.9 MB)
  • 7. Adaptive Boosting (Adaboost).srt (21.6 KB)
  • 8. Cascading.mp4 (12.1 MB)
  • 8. Cascading.srt (9.2 KB)
  • 9. Face Detection Intuition.html (0.1 KB)
3. Module 1 - Face Detection with OpenCV
  • 1. Welcome to the Practical Applications.mp4 (15.7 MB)
  • 1. Welcome to the Practical Applications.srt (8.1 KB)
  • 10. Face Detection with OpenCV.html (0.1 KB)
  • 2. Installations Instructions (once and for all!).mp4 (34.4 MB)
  • 2. Installations Instructions (once and for all!).srt (22.1 KB)
  • 3. Common Debug Tips.html (0.3 KB)
  • 3.1 Debug Solutions.pdf.pdf (127.8 KB)
  • 4. Face Detection - Step 1.mp4 (11.8 MB)
  • 4. Face Detection - Step 1.srt (9.9 KB)
  • 5. Face Detection - Step 2.mp4 (9.5 MB)
  • 5. Face Detection - Step 2.srt (7.7 KB)
  • 6. Face Detection - Step 3.mp4 (6.9 MB)
  • 6. Face Detection - Step 3.srt (5.1 KB)
  • 7. Face Detection - Step 4.mp4 (9.7 MB)
  • 7. Face Detection - Step 4.srt (6.9 KB)
  • 8. Face Detection - Step 5.mp4 (9.3 MB)
  • 8. Face Detection - Step 5.srt (6.8 KB)
  • 9. Face Detection - Step 6.mp4 (24.4 MB)
  • 9. Face Detection - Step 6.srt (14.9 KB)
4. Homework Challenge - Build a Happiness Detector
  • 1. Homework Challenge - Instructions.html (1.3 KB)
  • 2. Homework Challenge - Solution (Video).mp4 (49.8 MB)
  • 2. Homework Challenge - Solution (Video).srt (27.0 KB)
  • 3. Homework Challenge - Solution (Code files).html (0.1 KB)
  • 3.1 Homework.zip.zip (216.8 KB)
5. Module 2 - Object Detection Intuition
  • 1. Plan of attack.mp4 (3.5 MB)
  • 1. Plan of attack.srt (2.8 KB)
  • 2. How SSD is different.mp4 (23.8 MB)
  • 2. How SSD is different.srt (12.5 KB)
  • 3. The Multi-Box Concept.mp4 (27.0 MB)
  • 3. The Multi-Box Concept.srt (14.6 KB)
  • 4. Predicting Object Positions.mp4 (25.3 MB)
  • 4. Predicting Object Positions.srt (14.0 KB)
  • 5. The Scale Problem.mp4 (26.8 MB)
  • 5. The Scale Problem.srt (17.3 KB)
  • 6. Object Detection Intuition.html (0.1 KB)
6. Module 2 - Object Detection with SSD
  • 1. Object Detection - Step 1.mp4 (36.0 MB)
  • 1. Object Detection - Step 1.srt (13.5 KB)
  • 10. Object Detection - Step 10.mp4 (49.2 MB)
  • 10. Object Detection - Step 10.srt (24.0 KB)
  • 11. Training the SSD.html (0.6 KB)
  • 11.1 Training_SSD.zip.zip (2.7 GB)
  • 12. Object Detection with SSD.html (0.1 KB)
  • 2. Object Detection - Step 2.mp4 (9.2 MB)
  • 2. Object Detection - Step 2.srt (7.2 KB)
  • 3. Object Detection - Step 3.mp4 (13.1 MB)
  • 3. Object Detection - Step 3.srt (10.2 KB)
  • 4. Object Detection - Step 4.mp4 (16.1 MB)
  • 4. Object Detection - Step 4.srt (12.0 KB)
  • 5. Object Detection - Step 5.

Code:

  • udp://tracker.opentrackr.org:1337/announce
  • udp://p4p.arenabg.com:1337/announce
  • udp://tracker.leechers-paradise.org:6969/announce
  • udp://9.rarbg.to:2710/announce
  • udp://9.rarbg.me:2710/announce
  • udp://exodus.desync.com:6969/announce
  • udp://open.stealth.si:80/announce
  • udp://tracker.cyberia.is:6969/announce
  • udp://tracker.sbsub.com:2710/announce
  • udp://retracker.lanta-net.ru:2710/announce
  • udp://tracker.tiny-vps.com:6969/announce
  • udp://tracker.torrent.eu.org:451/announce
  • udp://tracker.moeking.me:6969/announce
  • udp://bt1.archive.org:6969/announce
  • http://tracker.nyap2p.com:8080/announce
  • udp://ipv4.tracker.harry.lu:80/announce
  • udp://bt2.archive.org:6969/announce
  • http://tracker3.itzmx.com:6961/announce
  • http://tracker1.itzmx.com:8080/announce
  • udp://explodie.org:6969/announce