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


Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs | Udemy
English | Size: 11.08 GB
Genre: eLearning

What you’ll learn
Learn by completing 26 advanced computer vision projects including Emotion, Age & Gender Classification, London Underground Sign Detection, Monkey Breed, Flowers, Fruits , Simpsons Characters and many more!
Advanced Deep Learning Computer Vision Techniques such as Transfer Learning and using pre-trained models (VGG, MobileNet, InceptionV3, ResNet50) on ImageNet and re-create popular CNNs such as AlexNet, LeNet, VGG and U-Net.
Understand how Neural Networks, Convolutional Neural Networks, R-CNNs , SSDs, YOLO & GANs with my easy to follow explanations
Become familiar with other frameworks (PyTorch, Caffe, MXNET, CV APIs), Cloud GPUs and get an overview of the Computer Vision World
How to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
How to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
How to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
How to use OpenCV with a FREE Optional course with almost 4 hours of video
How to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
How to use TensorFlow’s Object Detection API and Create A Custom Object Detector in YOLO
Facial Recognition with VGGFace
Use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPU
Build a Computer Vision API and Web App and host it on AWS using an EC2 Instance

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

TensorFlow Object Detection API

YOLO (DarkNet and DarkFlow)

OpenCV

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

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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!

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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

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