Mastering Advanced Representation Learning (CV)

Mastering Advanced Representation Learning (CV), available at $39.99, has an average rating of 3.25, with 61 lectures, 3 quizzes, based on 6 reviews, and has 123 subscribers.

You will learn about 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 This course is ideal for individuals who are Developer who are interested in building AI/Deep Learning products or Architects who are interested in building AI//Deep Learning products or Developer and AI Developer who are interested in Data Augmentation Technique or Developer and AI Developer who are interested in Production Level Computer Vision or Developer and AI Developer who are interested in Deep Learning, Deep Unsupervised Learning It is particularly useful for Developer who are interested in building AI/Deep Learning products or Architects who are interested in building AI//Deep Learning products or Developer and AI Developer who are interested in Data Augmentation Technique or Developer and AI Developer who are interested in Production Level Computer Vision or Developer and AI Developer who are interested in Deep Learning, Deep Unsupervised Learning.

Enroll now: Mastering Advanced Representation Learning (CV)

Summary

Title: Mastering Advanced Representation Learning (CV)

Price: $39.99

Average Rating: 3.25

Number of Lectures: 61

Number of Quizzes: 3

Number of Published Lectures: 57

Number of Published Quizzes: 3

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 60

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: ₹799

Quality Status: approved

Status: Live

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

Target Audiences

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

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 heavilyused 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 andDeep 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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview

Lecture 2: Applications

Lecture 3: Google Colab Setup

Lecture 4: Course Structure & Important Notes

Chapter 2: Data Science in Numpy & Pytorch (code) – Background

Lecture 1: Data Science in Numpy – Part1 (Code)

Lecture 2: Data Science in Pytorch – Part1 (Code)

Lecture 3: Data Science in Pytorch – Part 2(Code)

Chapter 3: Pytorch AutoGrad

Lecture 1: Pytorch AutoGrad

Lecture 2: Custom CNN in Pytorch

Chapter 4: Faiss & Image Search (Hands on, Dont skip)

Lecture 1: Image Search(Basic & Cluster)

Lecture 2: Faiss Overview

Lecture 3: Basic Image Search (Code)

Lecture 4: Basic Image Search With pertained Resnet (cifar-10 dataset) (Code)

Lecture 5: Cluster Search (Code)

Chapter 5: SOTA Data augmentation (Hands On)

Lecture 1: Why Data Augmentation & History

Lecture 2: CutMix Paper Overview

Lecture 3: Results of CutMix

Lecture 4: CutMix Algorithm

Lecture 5: CutMix (Code)

Lecture 6: RandAugment

Lecture 7: RandAugment (Code)

Chapter 6: Softmax think out of the box (Hands On)

Lecture 1: SoftMax Think out of the box

Lecture 2: Temperature Scaling & soft softmax (code)

Lecture 3: Summery

Chapter 7: Prelearing & UVR by Context Prediction (Theory)

Lecture 1: Pretext Task

Lecture 2: Overview of Unsupervised Visual Representation Learning by Context Prediction

Lecture 3: Results of UVR by Context Prediction

Chapter 8: JigSaw

Lecture 1: Overview of Jigsaw

Lecture 2: Network and Training process

Lecture 3: Results of JigSaw

Chapter 9: Non-Parametric Instance Level Discrimination(NPILD) (hands on)

Lecture 1: Non-Parametric Instance-level Discrimination & Metric learning approach

Lecture 2: NPILD Training Process

Lecture 3: Non Parametric Softmax

Lecture 4: Noise contrastive estimation (NCE) – Part 1

Lecture 5: FULL NCE Loss

Lecture 6: NPILD Put it all together

Lecture 7: NPILD Result

Lecture 8: Non Parametric Softmax (CrossEntropy) (Code)

Chapter 10: PEARL

Lecture 1: Self-Supervised Learning of Pretext-Invariant Representations (PEARL) – Part 1

Lecture 2: PEARL Overview Part 2

Lecture 3: PEARL Loss

Lecture 4: PEARL Results

Chapter 11: PEARL and NPILD (code)

Lecture 1: NCE & Memory Bank (Code)

Lecture 2: Network and Training NPILD & Pearl (Code)

Chapter 12: SimCLR

Lecture 1: SIMCLR Overview

Lecture 2: SIMCLR & Multiview Batch

Lecture 3: SimCLR Algorithm and Loss

Lecture 4: Training Details

Lecture 5: Softmax is invariant under translation (Important)

Chapter 13: SupCon & SimCLR (Code)

Lecture 1: Supervised Contrastive Learning

Lecture 2: Mocking SimCLR(Code)

Lecture 3: SimClr and Supervised Contrastive Learning (Code)

Chapter 14: Practice Test (Covering Upto DUVRL)

Chapter 15: Few More ideas in Visual Representation Learning

Lecture 1: Vissl & Albumentations

Lecture 2: Tips From My Expeience

Lecture 3: Few More ideas

Chapter 16: DeepFakes & Beyond – Second Part of the course(In-Progress)

Lecture 1: Introduction to DeepFake & Beyond

Lecture 2: Generative Vs Discriminative AI With VAE Example (will be separate course)

Instructors

samrat saha
Principal Data Scientist @ jio
Fish Ai
Stealth Mode Start up

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 1 votes
5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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