Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs

Description

Update: June-2020

• TensorFlow 2.0 Compatible Code

• Windows install guide for TensorFlow2.0 (with Keras), OpenCV4 and Dlib

Deep Learning Computer Vision™ Use Python & Keras to implement CNNs, YOLO, TFOD, R-CNNs, SSDs & GANs + A Free Introduction to OpenCV.

If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands  the following Deep Learning frameworks in Python:

• Keras

• Tensorflow 2.0

• TensorFlow Object Detection API

• YOLO (DarkNet and DarkFlow)

• OpenCV4

All in an easy to use virtual machine, with all libraries pre-installed!

======================================================

Apr 2019 Updates:

• How to set up a Cloud GPU on PaperSpace and Train a CIFAR10 AlexNet CNN almost 100 times faster!

• Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

Mar 2019 Updates:

Newly added Facial Recognition & Credit Card Number Reader Projects

• Recognize multiple persons using your webcam

• Facial Recognition on the Friends TV Show Characters

• Take a picture of a Credit Card, extract and identify the numbers on that card!

======================================================

Computer vision applications involving Deep Learning are booming!

Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to:

• Perform surgery and accurately analyze and diagnose you from medical scans.

• Enable self-driving cars

• Radically change robots allowing us to build robots that can cook, clean and assist us with almost any task

• Understand what's being seen in CCTV surveillance videos thus performing security, traffic management and a host of other services

• Create Art with amazing Neural Style Transfers and other innovative types of image generation

• Simulate many tasks such as Aging faces, modifying live video feeds and realistically replace actors in films

Huge technology companies such as Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily devoting billions to computer vision research.

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision with Deep Learning is hard!

• Tutorials are too technical and theoretical

• Code is outdated

• Beginners just don't know where to start

That's why I made this course!

• I  spent months developing a proper and complete learning path.

• I teach all key concepts logically and without overloading you with mathematical theory while using the most up to date methods. 

• I created a FREE Virtual Machine with all Deep Learning Libraries (Keras, TensorFlow, OpenCV, TFODI, YOLO, Darkflow etc) installed! This will save you hours of painfully complicated installs

• I teach using practical examples and you'll learn by doing 18 projects!

Projects such as:

1. Handwritten Digit Classification using MNIST

2. Image Classification using CIFAR10

3. Dogs vs Cats classifier

4. Flower Classifier using Flowers-17

5. Fashion Classifier using FNIST

6. Monkey Breed Classifier

7. Fruit Classifier

8. Simpsons Character Classifier

9. Using Pre-trained ImageNet Models to classify a 1000 object classes

10. Age, Gender and Emotion Classification

11. Finding the Nuclei in Medical Scans using U-Net

12. Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection

13. Object Detection with YOLO V3

14. A Custom YOLO Object Detector that Detects London Underground Tube Signs

15. DeepDream

16. Neural Style Transfers

17. GANs - Generate Fake Digits

18. GANs - Age Faces up to 60+ using Age-cGAN

19. Face Recognition

20. Credit Card Digit Reader

21. Using Cloud GPUs on PaperSpace

22. Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

And OpenCV Projects such as:

1. Live Sketch

2. Identifying Shapes

3. Counting Circles and Ellipses

4. Finding Waldo

5. Single Object Detectors using OpenCV

6. Car and Pedestrian Detector using Cascade Classifiers

So if you want to get an excellent foundation in Computer Vision, look no further.

This is the course for you!

In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

======================================================

As for Updates and support:

I will be active daily in the 'questions and answers' area of the course, so you are never on your own.    

So, are you ready to get started? Enroll now and start the process of becoming a Master in Computer Vision using Deep Learning today!

======================================================

What previous students have said my other Udemy Course: 

"I'm amazed at the possibilities. Very educational, learning more than what I ever thought was possible. Now, being able to actually use it in a practical purpose is intriguing... much more to learn & apply"

"Extremely well taught and informative Computer Vision course! I've trawled the web looking for OpenCV python tutorials resources but this course was by far the best amalgamation of relevant lessons and projects. Loved some of the projects and had lots of fun tinkering them."

"Awesome instructor and course. The explanations are really easy to understand and the materials are very easy to follow. Definitely a really good introduction to image processing."

"I am extremely impressed by this course!! I think this is by far the best Computer Vision course on Udemy. I'm a college student who had previously taken a Computer Vision course in undergrad. This 6.5 hour course blows away my college class by miles!!"

"Rajeev did a great job on this course. I had no idea how computer vision worked and now have a good foundation of concepts and knowledge of practical applications. Rajeev is clear and concise which helps make a complicated subject easy to comprehend for anyone wanting to start building applications."

======================================================

TAKE THIS COURSE