Mastering Advanced Representation Learning (CV)

Advanced Computer Vision, Production Level Representation Learning, Data Augmentation in Pytorch & Numpy

Ratings: 3.43 / 5.00




Description

Published in  2021

Currently Updating to include Representation Learning beyond 2021


You can take this course risk-free and if you don't like it, you can get a refund anytime in the first 30 days.

Welcome to the "Advanced CV Deep Representation Learning Data Augmentation and More in Pytorch & Numpy".


Deep Unsupervised Visual Representation Learning, Unsupervised computer vision in deep learning is very niche skill and it is being heavily used in production by AI superstar companies like Google, Amazon, Facebook, as a matter of fact lots of ideas we will talk about. In this course are being used to build SOTA products like Shop the Look or Face Search, Speech to emotion detection.

To learn Deep Learning and Deep Unsupervised Visual Representation learning,  step-by-step, you have come to the right place.


Deep Learning is Easy to learn, if you know basic Math and can code.

Thanks to my several years of experience in Deep Learning, I wanted to share my experience in Deep Representation Learning which are highly used in production level applications.

We'll take a step-by-step approach to learn all the fundamentals of Representation learning, Various kind of Visual Representation learning, SOTA data augmentations.


At the end of this course, you'll be productive and you'll know the following:

First Part

  • Unsupervised Visual Representation learning

  • Numpy

  • pytorch

  • pytorch Tensor API

  • pytorch Tensor Manipulation

  • pytorch Autograds and gradients

  • pytorch Vision training pipeline

  • torchvision pretrained model load

  • Image Search

  • Cluster Search

  • Faiss Search

  • PEARL

  • NPILD

  • JigSaw

  • Simple Contrastive learning

  • Supervised Contrastive learning

  • Self Supervised Contrastive learning


Note:  The Hands on section is written in python 3.6, pytorch, numpy which is defacto now a days for deep learning. But the concepts covered in the course is also applicable if you use tensorflow or other equivalent libraries.

Although the code is Computer Vision heavy but these ideas can also be applied to Speech and NLP.


You can take this course risk-free and if you don't like it, you can get a refund anytime in the first 30 days.


Instructor

The instructor of this course have more than 15+ years of experience in Machine learning and deep Learning, and worked with people from Google Brain team. The instructor also hold multiple patent in the area of machine learning and deep learning.

Fish AI is in stealth mode early stage start up as of 2021.


This Course Also Comes With

  • Lifetime Access to All Future Updates

  • A responsive instructor in the Q&A Section

  • Links to interesting articles, and lots of good code to base your next applications onto

  • Udemy Certificate of Completion Ready for Download


This is the course that could improve your career.

Computer vision is a niche skill. Especially if you know deep learning unsupervised approches.

All the papers and ideas presented in this course are used by production level AI products. the skills you acquire in this course will definitely help you in lots of computer vision applications.


I hope to see you inside the course.

Who this course is for

  • AI application Developers who want to built cool vision based applications

  • AI application Developers who want to learn unsupervised way of deep learning

  • Any Developers who wants to build face recognition, object detection, image search , apparel recognition, speech recognition based products

  • AI Architects who want to develop state of the art vision products

  • Anyone looking to learn the theory of deep unsupervised visual representation learning

Happy learning

What You Will Learn!

  • Representation Learning
  • Deep Learning
  • Industry Level Advanced Computer Vision
  • Awesome Data Augmentation techniques in pytorch
  • Various properties of Softmax and CrossEntropy in Numpy & Pytorch
  • State of the art methods like RandAug, JigSaw, PEARL, NPILD, SimCLR
  • SimCLR (Simple Contrastive Learning), Supervised contrastive learning
  • Faiss Search, Image Search and Cluster Search
  • Computer Vision
  • Unsupervised Supervised Self Supervised Visual Representation Learning Techniques
  • Image Search

Who Should Attend!

  • Developer who are interested in building AI/Deep Learning products
  • Architects who are interested in building AI//Deep Learning products
  • Developer and AI Developer who are interested in Data Augmentation Technique
  • Developer and AI Developer who are interested in Production Level Computer Vision
  • Developer and AI Developer who are interested in Deep Learning, Deep Unsupervised Learning