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

2020 Update with TensorFlow 2.0 Support. Become a Pro at Deep Learning Computer Vision! Includes 20+ Real World Projects

4.41 (1842 reviews)


14.5 hours


Jun 2020

Last Update
Regular Price

What you will 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


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."



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



Course Introduction

Introduction to Computer Vision & Deep Learning

Introduction to Computer Vision & Deep Learning

What is Computer Vision and What Makes it Hard

What are Images?

Intro to OpenCV, OpenVINO™ & their Limitations

Setup Your FREE Deep Learning Development Virtual Machine

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

Optional - Troubleshooting Guide for VM Setup & for resolving some MacOS Issues

Optional - Manual Setup of Ubuntu Virtual Machine

Optional - Setting up a shared drive with your Host OS

Handwriting Recognition, Simple Object Classification OpenCV Demo

Get Started! Handwriting Recognition, Simple Object Classification OpenCV Demo

Experiment with a Handwriting Classifier

Experiment with a Image Classifier

OpenCV Demo – Live Sketch with Webcam

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

Setup OpenCV

What are Images?

How are Images Formed

Storing Images on Computers

Getting Started with OpenCV - A Brief OpenCV Intro

Grayscaling - Converting Color Images To Shades of Gray

Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally

Histogram representation of Images - Visualizing the Components of Images

Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text

Transformations, Affine And Non-Affine - The Many Ways We Can Change Images

Image Translations - Moving Images Up, Down. Left And Right

Rotations - How To Spin Your Image Around And Do Horizontal Flipping

Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality

Image Pyramids - Another Way of Re-Sizing

Cropping - Cut Out The Image The Regions You Want or Don't Want

Arithmetic Operations - Brightening and Darkening Images

Bitwise Operations - How Image Masking Works

Blurring - The Many Ways We Can Blur Images & Why It's Important

Sharpening - Reverse Your Images Blurs

Thresholding (Binarization) - Making Certain Images Areas Black or White

Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines

Edge Detection using Image Gradients & Canny Edge Detection

Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down

Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing

Segmentation and Contours - Extract Defined Shapes In Your Image

Sorting Contours - Sort Those Shapes By Size

Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours

Matching Contour Shapes - Match Shapes In Images Even When Distorted

Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)

Line Detection - Detect Straight Lines E.g. The Lines On A Sudoku Game

Circle Detection

Blob Detection - Detect The Center of Flowers

Mini Project 3 - Counting Circles and Ellipses

Object Detection Overview

Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image)

Feature Description Theory - How We Digitally Represent Objects

Finding Corners - Why Corners In Images Are Important to Object Detection

Histogram of Oriented Gradients - Another Novel Way Of Representing Images

HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing

Face and Eye Detection - Detect Human Faces and Eyes In Any Image

Mini Project 6 - Car and Pedestrian Detection in Videos

Neural Networks Explained in Detail

Neural Networks Chapter Overview

Machine Learning Overview

Neural Networks Explained

Forward Propagation

Activation Functions

Training Part 1 – Loss Functions

Training Part 2 – Backpropagation and Gradient Descent

Backpropagation & Learning Rates – A Worked Example

Regularization, Overfitting, Generalization and Test Datasets

Epochs, Iterations and Batch Sizes

Measuring Performance and the Confusion Matrix

Review and Best Practices

Convolutional Neural Networks (CNNs) Explained in Detail

Convolutional Neural Networks Chapter Overview

Convolutional Neural Networks Introduction

Convolutions & Image Features

Depth, Stride and Padding



The Fully Connected Layer

Training CNNs

Designing Your Own CNN

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

Building a CNN in Keras

Introduction to Keras & Tensorflow

Building a Handwriting Recognition CNN

Loading Our Data

Getting our data in ‘Shape’

Hot One Encoding

Building & Compiling Our Model

Training Our Classifier

Plotting Loss and Accuracy Charts

Saving and Loading Your Model

Displaying Your Model Visually

Building a Simple Image Classifier using CIFAR10

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

Introduction to Visualizing What CNNs 'see' & Filter Visualizations

Saliency Maps & Class Activation Maps

Saliency Maps & Class Activation Maps

Filter Visualizations

Heat Map Visualizations of Class Activations

Data Augmentation: Build a Cats vs Dogs Classifier

Data Augmentation Chapter Overview

Splitting Data into Test and Training Datasets

Train a Cats vs. Dogs Classifier

Boosting Accuracy with Data Augmentation

Types of Data Augmentation

Confusion Matrix, Classification Report & Viewing Misclassifications

Introduction to the Confusion Matrix & Viewing Misclassifications

Understanding the Confusion Matrix

Finding and Viewing Misclassified Data

Types of Optimizers, Learning Rates & Callbacks: Build a Fruit Classifier

Introduction to the types of Optimizers, Learning Rates & Callbacks

Types Optimizers and Adaptive Learning Rate Methods

Keras Callbacks and Checkpoint, Early Stopping and Adjust Learning Rates that Pl

Build a Fruit Classifier

Batch Normalization & Build LeNet, AlexNet: Build a Fashion/Clothes Classifier

Intro to Building LeNet, AlexNet in Keras & Understand Batch Normalization

Build LeNet and test on MNIST

Build AlexNet and test on CIFAR10

Batch Normalization

Build a Clothing & Apparel Classifier (Fashion MNIST)

ImageNet in Keras (VGG16/19, InceptionV3, ResNet50) - Advanced Image Classiers

Chapter Introduction

ImageNet - Experimenting with pre-trained Models in Keras (VGG16, ResNet50, Mobi

Understanding VGG16 and VGG19

Understanding ResNet50

Understanding InceptionV3

Transfer Learning and Fine Tuning: Build a Flower and Monkey Breed Classifier

Chapter Introduction

What is Transfer Learning and Fine Tuning

Build a Monkey Breed Classifier with MobileNet using Transfer Learning

Build a Flower Classifier with VGG16 using Transfer Learning

Design Your Own CNN - LittleVGG: Build a Simpsons Character Classifier

Chapter Introduction

Introducing LittleVGG

Simpsons Character Recognition using LittleVGG

Advanced Activation Functions and Initializations

Chapter Introduction

Dying ReLU Problem and Introduction to Leaky ReLU, ELU and PReLUs

Advanced Initializations

Deep Surveillance: Build a Facial Emotion, Age & Gender Recognition System

Chapter Introduction

Build an Emotion, Facial Expression Detector

Build Emotion/Age/Gender Recognition in our Deep Surveillance Monitor

Image Segmentation & Medical Imaging in U-Net: Find Nuclei in Images

Chapter Overview on Image Segmentation & Medical Imaging in U-Net

What is Segmentation? And Applications in Medical Imaging

U-Net: Image Segmentation with CNNs

The Intersection over Union (IoU) Metric

Finding the Nuclei in Divergent Images

Principles of Object Detection

Chapter Introduction

Object Detection Introduction - Sliding Windows with HOGs

R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN

Single Shot Detectors (SSDs)


TensorFlow Object Detection API

Chapter Introduction

TFOD API Install and Setup

Experiment with a ResNet SSD on images, webcam and videos

How to Train a TFOD Model

Object Detection with YOLO & Darkflow: Build a London Underground Sign Detector

Chapter Introduction

Setting up and install Yolo DarkNet and DarkFlow

Experiment with YOLO on still images, webcam and videos

Build your own YOLO Object Detector - Detecting London Underground Signs

DeepDream & Neural Style Transfers: Make AI Generated Art

Chapter Introduction

DeepDream – How AI Generated Art All Started

Neural Style Transfer

Generative Adversarial Networks (GANs): Age Faces to 60+ Age with our Age-cGAN

Generative Adverserial Neural Networks Chapter Overview

Introduction To GANs

Mathematics of GANs

Implementing GANs in Keras

Face Aging GAN

Face Recognition with VGGFace

Basic Face Recognition using LittleVGG CNN

Face Matching with VGGFace

Face Recognition using WebCam & Identifying Friends TV Show Characters in Video

The Computer Vision World

Chapter Introduction

Alternative Frameworks: PyTorch, MXNet, Caffe, Theano & OpenVINO

Popular APIs Google, Microsoft, ClarifAI Amazon Rekognition and others

Popular Computer Vision Conferences & Finding Datasets

Building a Deep Learning Machine vs. Cloud GPUs

BONUS - Build a Credit Card Number Reader

Step 1 - Creating a Credit Card Number Dataset

Step 2 - Training Our Model

Step 3 - Extracting A Credit Card from the Background

Step 4 - Use our Model to Identify the Digits & Display it onto our Credit Card

BONUS - Use Cloud GPUs on PaperSpace

Why use Cloud GPUs and How to Setup a PaperSpace Gradient Notebook

Train a AlexNet on PaperSpace

BONUS - Create a Computer Vision API & Web App Using Flask and AWS

Install and Run Flask

Running Your Computer Vision Web App on Flask Locally

Running Your Computer Vision API

Setting Up An AWS Account

Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask

Changing your EC2 Security Group

Using FileZilla to transfer files to your EC2 Instance

Running your CV Web App on EC2

Running your CV API on EC2


Jeff21 September 2020

This is a well put together course. It was organized well and the instructor spoke clearly. The lessons were for the most part paced very well. They didn't spend too much time in detail or skim over important parts. The examples and code chosen were very good to demonstrate the concept.

Mohsen15 September 2020

I've learnt some minor stuff out of this whole course, that's why I don't ask for refund nor gave the course a 1 star. the instructor didn't bother to watch the course himself, sometimes the chapter is cut in the middle of the discussion and sometimes he is not satisfied how he started the recording and start over again and didn't bother to edit the video. And overall is a very superficial course. Just watch the SSD part and you will see my point.

Tufail29 July 2020

I have taken many courses on Udemy and Coursera on this topic and this course is better than all of them. I watched this course on one of friend's account and immediately bought this course (and all the other courses by Rajeev). Rajeev's way of explanation is just outstanding. One suggestion: In Object detection part you could have explained more on darkflow etc. specially on custom detection part.

Shivam13 July 2020

It has been a great course, there might be one thing that the author doesn't explain much of code, but one has to remember that he covers so many topics and it's worth it. Also, this course is for an individual who has knowledge of python fundamentals.

Karthik27 June 2020

atrocious, the course content seems to be good but it's a waste of time he will say Object detection works on VM well but the environment or the files he uploaded will not work. We have to wait for hours for the files to be downloaded because they are very big files and finally they will not work and if students ask about these issues he will say not to use VM now go for windows local machine and again after few days he will change his opinion and gives irrelevant answers to the questions i asked and responding time to Q&A is also very poor. He is responding after 3-4 days and meanwhile we will lose our interest. Finally i enrolled for another CV course on udemy

Maxim21 February 2020

Not enough hands on experience outside of official documentation tutorials of the libraries \ tools used

Anju25 December 2019

It covers many areas of Computer Vision. And so many projects to work out on my own pace means that I am equipped to start building my next project on my own. It gives a top-down view of the subject - the tools, the libraries, the top conferences in Computer Vision etc. It takes you from 0 to 100 and hence plugs all the holes in your understanding of the subject. Overall - a great course!

Judegerin20 December 2019

The introduction given is very clear and motivative. Hope it will help me to gain a good result , as I have already tried a few other courses which didn't meet my real objective, as it just taught too simple not covering the core concepts or too theoretical. Hopefully this course will give ample opportunity to get our hands dirt for implementing what we've learnt and also the creative ideas with fun to try own new models

Sherri15 December 2019

I absolutely love Rajeev's courses! I'm still using the virtual machine he gave us in this course. In fact, I prefer it much more to windows OS. He was able to take me from a complete beginner in deep learning, to someone with a very good grasp on deep learning concepts. But what I like best is how he breaks down the content with hands-on practice so you not only understand the intuition, but he also teaches how to apply it. Also, he is very responsive when you have questions. I just bought his Data Science and Deep Learning for Business class and can't wait to get started! Thank you so much Rajeev, and please keep the classes coming!

Muhammad25 November 2019

i am in section 5, so far so good. i know, many people dosent like if code is written before, but it make time more efficient. To remember just repeat it

Denis11 November 2019

It seems that in some cases the author is not prepared well for the lecture, like there are some mistakes in slides and he forgets some things, but in general it's good

Muawiyah11 November 2019

I love to read books, this tutorial, with its working code is more clear than tenth of books I read so far about Deep Learning. Suggestion: Convert the code so it can be tried online, such as on Google Colab.

Tendani11 November 2019

I enrolled in this course because I wanted a good start with computer vision. I got!! Thanks for putting all this together.

Kim25 October 2019

This is almost perfect. I suggest to everyone who is studying deep learning. These contents are greate. I think these are useful for someone who is Intermediate level also.

Andreas3 October 2019

Ziemlich chaotisch, wenig wirklich erklärt. Aber insgesamt ein guter Überblick mit vielen Hinweisen, wo man weitere Infos findet.


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