tathaghosh / Important-Websites-for-Machine-Learning

Important Blogs and Youtube Channels for Machine Learning:

Blogs

• Pratik Bhavsar(Pratik's Pakodas)
  • A Data Scientist Without A PhD
• Eugene Yan
  • NLP for Supervised Learning - A Brief Survey
• Amit Chaudhary(Amitness)
• Lena Voita
  • NLP Course | For You
• Robert Tjarko Lange
• Francis Bach
• Chris Olah
• Marcin Zabłocki
  • Understanding SimCLR framework
• Divam Gupta
  • Image Segmentation using Keras
• Sabrina J. Mielke
  • From PyTorch to JAX
• Raúl Gómez
• Guillaume Genthial
• Hong Jing (Jingles)
• Constanza Fierro
  • Summary: BERT
• Peter Bloem
  • Transformers from scratch
• Peter Roelants
• Venelin Valkov
• Kyunghyun Cho
• Chris McCormick
  • Tutorials
• Nipun Batra
• Jay Alammar
• Ayan Das
• Michael Phi
• Ehud Reiter
• Aman Arora
• Yasoob Khalid(Python)
  • Understanding and Decoding a JPEG Image using Python
• Distill
• dair.ai
• Off the Convex Path
• Autonomous Vision Blog
• Humboldt-University of Berlin
• The Berkeley Artificial Intelligence Research(BAIR) Blog
• Jordi Pons)
  • Deep neural networks for music
• Andrew Gibiansky
• Desh Raj
• Frank Dellaert
• Loren Lugosch
• Maël Fabien
• Sander Dieleman
• Jakub Tomczak
• Wessel Bruinsma
• Invenia Blog
• Alexia Jolicoeur-Martineau
  • The new contender to GANs
• Engraved-Jonas Degrave & Ira Korshunova
  • Why are machine learning algorithms hard to tune?
  • How we can make machine learning algorithms tunable
• Johannes Brandstetter(ML-JKU)
  • RUDDER: Return Decomposition for Delayed Rewards
  • Hopfield Networks is All You Need
  • Align-RUDDER: Learning from Few Demonstrations by Reward Redistribution
  • Cross-Domain Few-Shot Learning by Representation Fusion
  • Looking at the Performer from a Hopfield point of view
• Generable
• HuggingFace Blogs
• GTSAM
• Best tea in London (A blog on statistics and machine learning)
• Fabian Fuchs
• Neural Magic
• Lilian Weng
  • What are Diffusion Models?
  • Contrastive Representation Learning
  • Controllable Neural Text Generation
  • How to Train Really Large Models on Many GPUs?
• Javier Rodriguez Zaurin (infinitoml)
  • pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM
• argmax.ai
• Jindřich Libovický
• It's Neuronal Blog(Alex Williams)
• Costa Huang
  • The 32 Implementation Details of Proximal Policy Optimization (PPO) Algorithm
• Yassine Ouali
  • CVPR 2021: An Overview
• Perceiving Systems Blog
• Transformers in Vision (IAML Distill Blog)
• Eric Jang
• Dan's Blog
• Ondřej Cífka
• Yang Song
  • Generative Modeling by Estimating Gradients of the Data Distribution
• Chaitanya K. Joshi
  • Recent Advances in Deep Learning for Routing Problems
  • Recent Advances in Efficient and Scalable Graph Neural Networks
• The blog of Matthijs Hollemans about machine learning and AI
• gradient science
• Wu Lin (Yorker Lin)
• Yuge Shi
  • An incomplete and slightly outdated literature review on augmentation based self-supervise learning
• Gwern Branwen
  • The Scaling Hypothesis

YouTube

• Letiția Pârcălăbescu
• Alfredo Canziani
• Matthias Nießner
• Andreas Mueller
• Valerio Velardo - The Sound of AI
• Abhishek Thakur
• Yannic Kilcher
• Henry AI Labs
• Elvis Saravia
• Jeff Heaton
• Minsuk Heo 허민석
• Krishna D N
• Aladdin Persson
• The A.I. Hacker - Michael Phi
• Venelin Valkov
• Chris McCormick
• Weights & Biases
• CodeEmporium
• HuggingFace
• Hvass Laboratories
• DeepMind
• Sung Kim
• Dorien Herremans
• Micheleen Harris(rheartpython)
• Aurélien Géron
• Daniel Gordon
• Carnegie Mellon University Deep Learning
• Pascal Poupart
• Data Science Courses (Ali Ghodsi-University of Waterloo)
• Paul Hand
• Intelligent Systems Lab
• Kapil Sachdeva
• Ari Seff
• Hung-yi Lee
• Shusen Wang
• Machine Learning & Simulation
• Neural Magic
• mildlyoverfitted
• Mutual Information