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

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
  • TensorFlow Object Detection API
  • YOLO (DarkNet and DarkFlow)
  • OpenCV

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

Apr 2019 Updates:

  • How to setup 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  • Code is outdated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.

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 4/2019

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Source: https://www.udemy.com/master-deep-learning-computer-visiontm-cnn-ssd-yolo-gans/

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