As the name tells, this repository contains the 100 Days of Machine Learning Coding as proposed by Siraj Raval. This repository provides you with complete knowledge on machine learning algorithms and maths behind them with add-ons cheatsheets and code.
This repository is maintained by Avik Jain and has more than 30,000 stars on Github.
A complete hands-on daily guide on studying to become a self-taught Machine Learning Engineer for those who come from a no CS background. This repository is unconventional because it’s the top-down and results-first approach designed for software engineers.
This repository is maintained by Nam Vu and has more than 24,000 stars on Github.
This repository contains a curated list of most cited Deep Learning papers in the year 2012–2016. Rather than providing an overwhelming amount of papers, this repository offers a curated list of the awesome deep learning papers which are a must-read in certain research domains.
The papers are well documented and categorized according to their topics, number of citations and year of publication.
1. Fundamentals of AI, ML and Deep Learning for Product Managers
2. The Unfortunate Power of Deep Learning
3. Graph Neural Network for 3D Object Detection in a Point Cloud
4. Know the biggest Notable difference between AI vs. Machine Learning
This repository is maintained by Terry Taewoong Um and has more than 22,000 stars on Github.
Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers.
Virgilio is your new Mentor for Data Science E-Learning aiming to mentor and guides anyone in the world of Data Science and Machine Learning through a series of curated articles, guides and tutorials.
Virgilio is maintained by a list of contributors, you can check them here, currently, Virgilio has more than 12,000 stars on Github.
Awesome NLP is a curated list of resources dedicated to Natural Language Processing (NLP). It contains resources to various Research Summaries, NLP Libraries, Video Tutorials, MOOCS, textbooks, links to the dataset for NLP tasks and many more.
It is maintained by Keon and has more than 10,000 stars on Github.
It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. The repository contains multiple datasets, models and Jupiter notebook files to assist you to train and download the dataset and models.
T2T was developed by researchers and engineers in the Google Brain team and a community of users and has more than 10,000 stars on Github.
This repository maintains the VIP cheatsheets for important notions that are covered in Stanford’s CS (221, 229, 230) Artificial Intelligence, Machine Learning and Deep Learning course all at the same place and has more than 10,000 stars on Github.
This is one of the Best Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP videos lectures maintained by the GodFather of deep learning “Geoffrey Hinton” and has more than 8,000 stars on Github.
Sir Hinton has invented several foundational deep learning techniques throughout life and currently is working for the University of Toronto and Google Brain.
This repository maintains all kinds of text classification models with deep learning. The aim behind this repo is to explore text classification methods in NLP with deep learning. It contains all kinds of baseline models for text classification and also supports for multi-label classification.
This repository is maintained by Liang Xu and has more than 6,000 stars on Github.
It contains a list of some best and awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general with resources to other subfields of transfer learning. It contains papers, books, few datasets tutorials, blogs, libraries, etc.
This repository is maintained by Arthur Pesah and has more than 1,200 stars on Github.