4.37 out of 5
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401 reviews on Udemy

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

Go from beginner to Expert in using Deep Learning for Computer Vision (Keras & Python) completing 28 Real World Projects
Instructor:
Rajeev Ratan
2,540 students enrolled
English [Auto-generated]
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!
Learn 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
Learn how to use the Python library Keras to build complex Deep Learning Networks (using Tensorflow backend)
Learn how to do Neural Style Transfer, DeepDream and use GANs to Age Faces up to 60+
Learn how to create, label, annotate, train your own Image Datasets, perfect for University Projects and Startups
Learn how to use OpenCV with a FREE Optional course with almost 4 hours of video
Learn how to use CNNs like U-Net to perform Image Segmentation which is extremely useful in Medical Imaging application
Learn how to use TensorFlow's Object Detection API and Create A Custom Object Detector in YOLO
Learn Facial Recognition with VGGFace
Learn to use Cloud GPUs on PaperSpace for 100X Speed Increase vs CPU
Learn to 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 OpenCV3.

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:

  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!

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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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Introduction

1
Course Introduction

An introduction to Computer Vision and Deep Learning. Learn how Deep Learning is changing the world and why you need to do this course.

Introduction to Computer Vision & Deep Learning

1
Introduction to Computer Vision & Deep Learning

Introduction to Computer Vision & Deep Learning chapter overview.

2
What is Computer Vision and What Makes it Hard

Learn what makes Computer Vision so hard.

3
What are Images?

Learn what exactly are images and how computers store and interpret image data.

4
Intro to OpenCV, OpenVINO™ & their Limitations

Learn about OpenCV, OpenVINO, what they're used for and their limitations.

Setup Your FREE Deep Learning Development Virtual Machine

1
Setting up your Deep Learning Virtual Machine (Download Code, VM & Slides here!)

How to set up your Deep Learning Ubuntu Virtual Machine

2
Optional - Troubleshooting Guide for VM Setup & for resolving some MacOS Issues
3
Optional - Manual Setup of Ubuntu Virtual Machine
4
Optional - Setting up a shared drive with your Host OS

Handwriting Recognition, Simple Object Classification OpenCV Demo

1
Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo
2
Experiment with a Handwriting Classifier

Run your own (pre-trained) handwritten digit classification on a real world image!

3
Experiment with a Image Classifier

Classify 10 Types of Images using the CIFAR10 Dataset

4
OpenCV Demo – Live Sketch with Webcam

Run a simple but fun OpenCV Demo that turns your webcam feed into a live sketch!

OpenCV3 Tutorial (OPTIONAL) - Live Sketches, Identify Shapes & Face Detection

1
Setup OpenCV
2
What are Images?
3
How are Images Formed
4
Storing Images on Computers
5
Getting Started with OpenCV - A Brief OpenCV Intro
6
Grayscaling - Converting Color Images To Shades of Gray
7
Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
8
Histogram representation of Images - Visualizing the Components of Images
9
Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text
10
Transformations, Affine And Non-Affine - The Many Ways We Can Change Images
11
Image Translations - Moving Images Up, Down. Left And Right
12
Rotations - How To Spin Your Image Around And Do Horizontal Flipping
13
Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
14
Image Pyramids - Another Way of Re-Sizing
15
Cropping - Cut Out The Image The Regions You Want or Don't Want
16
Arithmetic Operations - Brightening and Darkening Images
17
Bitwise Operations - How Image Masking Works
18
Blurring - The Many Ways We Can Blur Images & Why It's Important
19
Sharpening - Reverse Your Images Blurs
20
Thresholding (Binarization) - Making Certain Images Areas Black or White
21
Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
22
Edge Detection using Image Gradients & Canny Edge Detection
23
Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down
24
Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing
25
Segmentation and Contours - Extract Defined Shapes In Your Image
26
Sorting Contours - Sort Those Shapes By Size
27
Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
28
Matching Contour Shapes - Match Shapes In Images Even When Distorted
29
Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
30
Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game
31
Circle Detection

Identify circles in an image

32
Blob Detection - Detect The Center of Flowers
33
Mini Project 3 - Counting Circles and Ellipses
34
Object Detection Overview
35
Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image)
36
Feature Description Theory - How We Digitally Represent Objects
37
Finding Corners - Why Corners In Images Are Important to Object Detection
38
Histogram of Oriented Gradients - Another Novel Way Of Representing Images
39
HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing
40
Face and Eye Detection - Detect Human Faces and Eyes In Any Image
41
Mini Project 6 - Car and Pedestrian Detection in Videos

Neural Networks Explained in Detail

1
Neural Networks Chapter Overview

Neural Networks Chapter Overview

2
Machine Learning Overview

A Brief introduction to Machine Learning, the types of Machine Learning and the ML process.

3
Neural Networks Explained

Understand Forward Propagation

4
Forward Propagation

Understand Forward Propagation


5
Activation Functions

Understand Activation Functions and why they're needed.


6
Training Part 1 – Loss Functions

Understand the importance of Loss Functions.


7
Training Part 2 – Backpropagation and Gradient Descent

Understand the importance of Backpropagation.


8
Backpropagation & Learning Rates – A Worked Example

Work through the Math of Backpropagation

9
Regularization, Overfitting, Generalization and Test Datasets
  • Understand Regularization, Overfitting and Generalization

  • Why we need Test Data

10
Epochs, Iterations and Batch Sizes

Understand the terms Epochs, Iterations and Batch Sizes

11
Measuring Performance and the Confusion Matrix

Know how to assess your NN's performance by understanding Classification Reports and the Confusion Matrix.

12
Review and Best Practices

Chapter review and the best practices or rules of thumb when it comes to training a Neural Network.

Convolutional Neural Networks (CNNs) Explained in Detail

1
Convolutional Neural Networks Chapter Overview

Convolutional Neural Networks Chapter Overview

2
Convolutional Neural Networks Introduction

Introduction to Convolutional Neural Networks

3
Convolutions & Image Features

What are Convolutions, Image Features and Feature Maps.

4
Depth, Stride and Padding

What is depth, stride and pooling and how they relate to feature maps generation.

5
ReLU

Understand how ReLU works

6
Pooling

Understand the importance of the Pooling or downsampling layer.

7
The Fully Connected Layer

Understand the importance of the Fully Connected or Dense Layer


8
Training CNNs

Understand what goes on when training a CNN

9
Designing Your Own CNN

How do we go about creating our own CNN designs

Build CNNs in Python using Keras - Handwriting Recognition (MNIST)

1
Building a CNN in Keras

Building a CNN in Keras   

2
Introduction to Keras & Tensorflow

Overview on Keras and TensorFlow

3
Building a Handwriting Recognition CNN

How to build a Handwriting Recognition classifier CNN in Python using Keras

4
Loading Our Data

How to load datasets into Python

5
Getting our data in ‘Shape’

How to preprocess your data to work with Keras

6
Hot One Encoding

What is hot-one-encoding and why it's needed.

7
Building & Compiling Our Model

How to build and compile our models

8
Training Our Classifier

How the training process works in Keras

9
Plotting Loss and Accuracy Charts

How to plot our Loss and Accuracy graphs

10
Saving and Loading Your Model

How to save and load saved your models

11
Displaying Your Model Visually

How to plot a visual representation of your model

12
Building a Simple Image Classifier using CIFAR10

Building a Simple Image Classifier using CIFAR10

What CNNs 'see' - Learn to do Filter Visualizations, Heatmaps and Salience Maps

1
Introduction to Visualizing What CNNs 'see' & Filter Visualizations

Visualizing What CNNs 'see' & Filter Visualizations chapter overview

2
Saliency Maps & Class Activation Maps

What are and how to plot Saliency Maps & Class Activation Maps   

3
Saliency Maps & Class Activation Maps

How to create Filter Visualizations

4
Filter Visualizations
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