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

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

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Code is outdated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_

_."_ ======================================================