Advanced Computer Vision Replearning, Vae, Gan, Deepfake +

[mitsumi]

Last updated 12/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.48 GB | Duration: 3h 38m

Advanced CV Deep Representation Learning, Transformer, Data Augmentation VAE, GAN, DEEPFAKE +More in Pytorch & Numpy

What you'll learn
Representation Learning
Deep Unsupervised/Supervised/Self Supervised Visual Representation Learning Techniques
Industry Level Advanced Computer Vision
Awesome SOTA 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, SupCon and many more..
SimCLR (Simple Contrastive Learning), Supervised contrastive learning
Faiss Search, Image Search and Cluster Search
noise contrastive estimator
Visual Transformers
AutoEncoders, VAE, GAN
DeepFake
Requirements
Desire to learn something awesome and new!
Description
Published in 2021: Alpha ReleaseYou 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, Transformer, Data Augmentation VAE, GAN, DEEPFAKE +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 PartUnsupervised Visual Representation learningNumpypytorchpytorch Tensor APIpytorch Tensor Manipulationpytorch Autograds and gradientspytorch Vision training pipelinetorchvision pretrained model loadImage SearchCluster SearchFaiss SearchPEARL NPILDJigSaw Simple Contrastive learningSupervised Contrastive learningSelf Supervised Contrastive learningPart 2VAEGANDEEPFakeNote: 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!InstructorThe 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 UpdatesA responsive instructor in the Q&A SectionLinks to interesting articles, and lots of good code to base your next applications ontoUdemy Certificate of Completion Ready for DownloadThis 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 applicationsAI 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 productsAI Architects who want to develop state of the art vision productsAnyone looking to learn the theory of deep unsupervised visual representation learningHappy learning!
Overview
Section 1: Introduction
Lecture 1 Course Overview
Lecture 2 Applications
Lecture 3 Google Colab Setup
Lecture 4 Course Structure & Important Notes
Section 2: Data Science in Numpy & Pytorch (code) - Background
Lecture 5 Data Science in Numpy - Part1 (Code)
Lecture 6 Data Science in Pytorch - Part1 (Code)
Lecture 7 Data Science in Pytorch - Part 2(Code)
Section 3: Pytorch AutoGrad
Lecture 8 Pytorch AutoGrad
Lecture 9 Custom CNN in Pytorch
Section 4: Faiss & Image Search (Hands on, Dont skip)
Lecture 10 Image Search(Basic & Cluster)
Lecture 11 Faiss Overview
Lecture 12 Basic Image Search (Code)
Lecture 13 Basic Image Search With pertained Resnet (cifar-10 dataset) (Code)
Lecture 14 Cluster Search (Code)
Section 5: SOTA Data augmentation (Hands On)
Lecture 15 Why Data Augmentation & History
Lecture 16 CutMix Paper Overview
Lecture 17 Results of CutMix
Lecture 18 CutMix Algorithm
Lecture 19 CutMix (Code)
Lecture 20 RandAugment
Lecture 21 RandAugment (Code)
Section 6: Softmax think out of the box (Hands On)
Lecture 22 SoftMax Think out of the box
Lecture 23 Temperature Scaling & soft softmax (code)
Lecture 24 Summery
Section 7: Prelearing & UVR by Context Prediction (Theory)
Lecture 25 Pretext Task
Lecture 26 Overview of Unsupervised Visual Representation Learning by Context Prediction
Lecture 27 Results of UVR by Context Prediction
Section 8: JigSaw
Lecture 28 Overview of Jigsaw
Lecture 29 Network and Training process
Lecture 30 Results of JigSaw
Section 9: Non-Parametric Instance Level Discrimination(NPILD) (hands on)
Lecture 31 Non-Parametric Instance-level Discrimination & Metric learning approach
Lecture 32 NPILD Training Process
Lecture 33 Non Parametric Softmax
Lecture 34 Noise contrastive estimation (NCE) - Part 1
Lecture 35 FULL NCE Loss
Lecture 36 NPILD Put it all together
Lecture 37 NPILD Result
Lecture 38 Non Parametric Softmax (CrossEntropy) (Code)
Section 10: PEARL
Lecture 39 Self-Supervised Learning of Pretext-Invariant Representations (PEARL) - Part 1
Lecture 40 PEARL Overview Part 2
Lecture 41 PEARL Loss
Lecture 42 PEARL Results
Section 11: PEARL and NPILD (code)
Lecture 43 NCE & Memory Bank (Code)
Lecture 44 Network and Training NPILD & Pearl (Code)
Section 12: SimCLR
Lecture 45 SIMCLR Overview
Lecture 46 SIMCLR & Multiview Batch
Lecture 47 SimCLR Algorithm and Loss
Lecture 48 Training Details
Lecture 49 Softmax is invariant under translation (Important)
Section 13: SupCon & SimCLR (Code)
Lecture 50 Supervised Contrastive Learning
Lecture 51 Mocking SimCLR(Code)
Lecture 52 SimClr and Supervised Contrastive Learning (Code)
Section 14: Practice Test (Covering Upto DUVRL)
Section 15: Few More ideas in Visual Representation Learning
Lecture 53 Vissl & Albumentations
Lecture 54 Tips From My Expeience
Lecture 55 Few More ideas
Section 16: DeepFakes & Beyond - Second Part of the course(In-Progress)
Lecture 56 Introduction to DeepFake & Beyond
Lecture 57 Generative Vs Discriminative AI With VAE Example (will be separate course)
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 Computer Vision, Deep Learning, Deep Unsupervised Learning

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