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!

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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!

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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   Code is outdated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:

   Handwritten Digit Classification using MNIST
   Image Classification using CIFAR10
   Dogs vs Cats classifier
   Flower Classifier using Flowers-17
   Fashion Classifier using FNIST
   Monkey Breed Classifier
   Fruit Classifier
   Simpsons Character Classifier
   Using Pre-trained ImageNet Models to classify a 1000 object classes
   Age, Gender and Emotion Classification
   Finding the Nuclei in Medical Scans using U-Net
   Object Detection using a ResNet50 SSD Model built using TensorFlow Object Detection
   Object Detection with YOLO V3
   A Custom YOLO Object Detector that Detects London Underground Tube Signs
   DeepDream
   Neural Style Transfers
   GANs – Generate Fake Digits
   GANs – Age Faces up to 60+ using Age-cGAN
   Face Recognition
   Credit Card Digit Reader
   Using Cloud GPUs on PaperSpace
   Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance!

And OpenCV Projects such as:

   Live Sketch
   Identifying Shapes
   Counting Circles and Ellipses
   Finding Waldo
   Single Object Detectors using OpenCV
   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.

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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!

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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.”

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Who this course is for:

   Programmers, college students or anyone enthusiastic about computer vision and deep learning
   Those wanting to be on the forefront of the job market for the AI Revolution
   Those who have an amazing startup or App idea involving computer vision
   Enthusiastic hobbyists wanting to build fun Computer Vision applications

Requirements

   Basic programming knowledge is a plus but not a requirement
   High school level math, College level would be a bonus
   Atleast 20GB storage space for Virtual Machine and Datasets
   A Windows, MacOS or Linux OS

Last Updated 6/2020