Rajeev D. Ratan
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:
Instructor Details
Courses : 2
Specification: Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
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9 reviews for Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs
Add a review
| Price | $19.99 |
|---|---|
| Provider | |
| Duration | 14.5 hours |
| Year | 2020 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
$19.99
Bita Jan Feshan –
many details are skipped, it would be better to be explained in the videos.
Ponnekanti S V Krishna –
Basic
Simon Martens –
I already took several courses about deep learning on Udemy in order to get different perspectives. This one is clearly the best. I love the concept and the way the instructor explains everything.
Prince Kumar –
Covering most of important concept in Computer Vision
Veera Venkata Raju Saladi –
Well planned detail course with very good practical exposure.
Don B –
It took a full 24 hours to get the linux image to work in VirtualBox, the lecturer is more focused on telling us what is not included in the course, and less with imparting useful nuggets. Poor sound quality in parts, ipython is not stable thus, including my points on virtual box & ipython, there is a reliance on software that is simply too fiddly to get value from.
Shivam Kumar –
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.
Tufail waris –
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.
Prasoon Dhaneshwar –
Covers essential topics in Keras on how to build models. Resources provided are very good. It’d be great if all errors are addressed, especially compatibility errors which I saw in later section of the course. Overall I highly recommend it! Great for beginners and for a nice revision for people working in the domain.