ai-links

This is the list of links on Deep Learning that I have collected over time.

Resources

1. An introduction to Generative Adversarial Networks (with code in TensorFlow) - AYLIEN News API
2. Generative Models
3. Adverarial Nets
4. Generative Adversarial Nets in TensorFlow - Agustinus Kristiadi
5. A (Very) Gentle Introduction to Generative Adversarial Networks (a.k…
6. Eric Jang: Generative Adversarial Nets in TensorFlow (Part I)
7. Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode | by Arthur Juliani | Medium
8. YouTube - Active One-shot Learning
9. 1605.06065 One-shot Learning with Memory-Augmented Neural Networks
10. Differential neural computer from DeepMind and more advances in backward propagation
11. Google’s DeepMind AI Now Capable of ‘Deep Neural Reasoning’ – The New Stack
12. Tutorial - What is a variational autoencoder? – Jaan Altosaar
13. Variational Autoencoders Explained
14. Under the Hood of the Variational Autoencoder (in Prose and Code)
15. Variational Autoencoder in TensorFlow
16. Eric Jang: Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax
17. Implementing Dynamic memory networks · YerevaNN
18. Variational Autoencoder (VAE) in Pytorch - Agustinus Kristiadi
19. PyTorch quick start: Classifying an image — Outcome Blog documentation
20. An end to end implementation of a Machine Learning pipelinet
21. 1602.05568 Multi-layer Representation Learning for Medical Concepts
22. 1605.03481 Tweet2Vec: Character-Based Distributed Representations for Social Media
23. 1603.07012 Semi-supervised Word Sense Disambiguation with Neural Models
24. 1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
25. 1704.08847 Parseval Networks: Improving Robustness to Adversarial Examples
26. SoundNet: Learning Sound Representations from Unlabeled Video - MIT
27. DeepMoji
28. Introduction to Machine Learning Interviews Book · MLIB
29. Fastcore - Fast.ai
30. Explained AI
31. Schedule « AGI-21: SF Bay Area and Virtual, Oct. 15-18, 2021
32. Machine Learning Crash Course  |  Google Developers
33. Stanford CRFM
34. HuBERT: How to Apply BERT to Speech, Visually Explained | Jonathan Bgn
35. Python Numpy Tutorial (with Jupyter and Colab)
36. Stanford DAWN · DAWN
37. 2022 AGI Safety Fundamentals alignment curriculum
38. Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
39. Deep Learning Links
40. MLExpert | MLExpert - land your dream Machine Learning job
41. Cloudera Fast Forward Blog
42. Design Patterns in Machine Learning Code and Systems
43. The Illustrated Machine Learning Website

Deep Learning Repositories

1. GitHub - teddykoker/tinyloader
2. GitHub - karpathy/micrograd: A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
3. GitHub - Renovamen/flint: A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
4. GitHub - Renovamen/Text-Classification: PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类
5. GitHub - Renovamen/metallic: A clean, lightweight and modularized PyTorch meta-learning library.
6. GitHub - graph4ai/graph4nlp: Graph4nlp is the library for the easy use of Graph Neural Networks for NLP
7. GitHub - maziarraissi/Applied-Deep-Learning: Applied Deep Learning
8. GitHub - dair-ai/ML-YouTube-Courses: A repository to index and organize the latest machine learning courses found on YouTube.
9. GitHub - NVIDIA/DeepLearningExamples: Deep Learning Examples
10. GitHub - rossant/awesome-math: A curated list of awesome mathematics resources
11. GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
12. GitHub - tmabraham/awesome-fastai: A curated list of awesome fastai projects/blog posts/tutorials/etc.
13. GitHub - booknlp/booknlp: BookNLP, a natural language processing pipeline for books
14. GitHub - amitness/learning: Becoming better at data science every day
15. GitHub - dair-ai/Transformers-Recipe: A quick recipe to learn all about Transformers
16. GitHub - minitorch/minitorch: The full minitorch student suite.
17. GitHub - rmcelreath/stat_rethinking_2022: Statistical Rethinking course winter 2022
18. GitHub - qdrant/awesome-metric-learning: 😎 A curated list of awesome practical Metric Learning and its applications
19. GitHub - kurtispykes/Natural-Language-Processing: Curated articles and code on NLP
20. GitHub - kurtispykes/Deep-Learning: Curated articles and code on deep learning topics
21. GitHub - lucidrains/DALLE2-pytorch: Implementation of DALL-E 2, OpenAI’s updated text-to-image synthesis neural network, in Pytorch
22. GitHub - ivan-bilan/The-NLP-Pandect: A comprehensive reference for all topics related to Natural Language Processing
23. GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
24. GitHub - anantzoid/VQA-Keras-Visual-Question-Answering: Visual Question Answering task written in Keras that answers questions about images
25. GitHub - carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow
26. GitHub - ryankiros/skip-thoughts: Sent2Vec encoder and training code from the paper “Skip-Thought Vectors”
27. GitHub - btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)
28. GitHub - speechbrain/speechbrain: A PyTorch-based Speech Toolkit
29. GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles
30. GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
31. GitHub - dennybritz/deeplearning-papernotes: Summaries and notes on Deep Learning research papers
32. GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles
33. GitHub - Synthaze/EpyNN: Educational python for Neural Networks.
34. GitHub - Nyandwi/machine_learning_complete: A comprehensive repository containing 30+ notebooks on learning machine learning!
35. GitHub - kenjihiranabe/The-Art-of-Linear-Algebra: Graphic notes on Gilbert Strang’s “Linear Algebra for Everyone”
36. GitHub - dair-ai/ML-Notebooks: A series of code examples for all sorts of machine learning tasks and applications.
37. GitHub - khuyentran1401/Data-science: Collection of useful data science topics along with code and articles
38. GitHub - Ying1123/awesome-neural-symbolic: A list of awesome neural symbolic papers.
39. GitHub - CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience
40. GitHub - hollance/neural-engine: Everything we actually know about the Apple Neural Engine (ANE)
41. https://github.com/karpathy/minGPT
42. GitHub - NielsRogge/Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.
43. GitHub - louisfb01/best_AI_papers_2022: A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.
44. GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.
45. GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML
46. GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
47. GitHub - dair-ai/Transformers-Recipe: 🧠 A study guide to learn about Transformers

Interpretability

1. GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.
2. GitHub - cdpierse/transformers-interpret: Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
3. GitHub - g8a9/ferret: A python package for benchmarking interpretability techniques.
4. A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist
5. GitHub - neelnanda-io/TransformerLens

Publications, Annotations and Visualizations

1. A Mathematical Framework for Transformer Circuits
2. labml.ai Annotated PyTorch Paper Implementations
3. explained.ai
4. GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, …), optimizers (Adam, adabelief, …), gans(cyclegan, stylegan2, …), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, … 🧠
5. Transformers from Scratch
6. Pandas Tutor - visualize Python pandas code
7. The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
8. You don’t know JAX
9. The Annotated Transformer
10. Differentiable Programming from Scratch – Max Slater – Computer Graphics, Programming, and Math
11. Logistic Regression
12. The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time.
13. CS 221 ― Artificial Intelligence - Cheat Sheets
14. Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - PDF
15. GPT in 60 Lines of NumPy | Jay Mody
16. Annotated S4
17. Attention, Transformers, in Neural Network Large Language Models
18. Transformer Circuits
19. Transformers from Scratch
20. OpenAI Microscope
21. A Visual Guide to Vision Transformers
22. LLM Visualization
23. The Illustrated AlphaFold
24. A Visual Guide to Quantization

Courses

1. Natural Language Processing (NLP) for Semantic Search | Pinecone
2. Syllabus for Mathematical Background for Machine Learning
3. Linear Algebra | Mathematics | MIT OpenCourseWare
4. Deep Learning for Natural Language Processing
5. Stanford CS 224N | Natural Language Processing with Deep Learning
6. A visual introduction to machine learning
7. GitHub - AMAI-GmbH/AI-Expert-Roadmap: Roadmap to becoming an Artificial Intelligence Expert in 2022
8. Deep Learning for Particle Physicists — Deep Learning for Particle Physicists
9. Neural networks and deep learning
10. AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube
11. GitHub - microsoft/AI-For-Beginners: 12 Weeks, 24 Lessons, AI for All!
12. First Principles of Computer Vision - YouTube
13. UNIGE 14x050 – Deep Learning
14. Deep Learning Systems
15. GitHub - karpathy/nn-zero-to-hero: Neural Networks: Zero to Hero
16. Home - Made With ML
17. GitHub - full-stack-deep-learning/fsdl-text-recognizer-2022-labs: Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
18. Cornell CS4780 - Machine Learning for Intelligent Systems
19. “Crash Course” - ML@B Blog Berkeley
20. Natural Language Processing Demystified
21. Deep Learning Fundamentals - Lightning AI
22. TinyML and Efficient Deep Learning Computing
23. GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book
24. 

MLOps

1. GitHub - dair-ai/MLOPs-Primer: A collection of resources to learn about MLOPs.

Industry Related

1. GitHub - andrewekhalel/MLQuestions: Machine Learning and Computer Vision Engineer - Technical Interview Questions
2. GitHub - BoltzmannEntropy/interviews.ai: It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

Contribute

To add links to this repository that you think are in any of the given category, please raise a PR or an issue will suffice as well.