ai-links
This is the list of links on Deep Learning that I have collected over time and still collecting.
https://github.com/khushmeeet/ai-links
Last synced: 8 days ago
JSON representation
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Courses
- Natural Language Processing (NLP) for Semantic Search | Pinecone
- Syllabus for Mathematical Background for Machine Learning
- Deep Learning for Natural Language Processing
- Stanford CS 224N | Natural Language Processing with Deep Learning
- A visual introduction to machine learning
- Deep Learning for Particle Physicists — Deep Learning for Particle Physicists
- Neural networks and deep learning
- AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube
- First Principles of Computer Vision - YouTube
- UNIGE 14x050 – Deep Learning
- Deep Learning Systems
- Home - Made With ML
- Cornell CS4780 - Machine Learning for Intelligent Systems
- “Crash Course” - ML@B Blog Berkeley
- Natural Language Processing Demystified
- Deep Learning Fundamentals - Lightning AI
- TinyML and Efficient Deep Learning Computing
- GitHub - AMAI-GmbH/AI-Expert-Roadmap: Roadmap to becoming an Artificial Intelligence Expert in 2022
- Neural networks and deep learning
- AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube
- GitHub - microsoft/AI-For-Beginners: 12 Weeks, 24 Lessons, AI for All!
- GitHub - karpathy/nn-zero-to-hero: Neural Networks: Zero to Hero
- “Crash Course” - ML@B Blog Berkeley
- GitHub - stas00/ml-engineering: Machine Learning Engineering Online Book
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Deep Learning Repositories
- GitHub - rossant/awesome-math: A curated list of awesome mathematics resources
- GitHub - teddykoker/tinyloader
- GitHub - karpathy/micrograd: A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
- GitHub - Renovamen/flint: A toy deep learning framework implemented in pure Numpy from scratch. Aka homemade PyTorch lol.
- GitHub - Renovamen/Text-Classification: PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类
- GitHub - Renovamen/metallic: A clean, lightweight and modularized PyTorch meta-learning library.
- GitHub - graph4ai/graph4nlp: Graph4nlp is the library for the easy use of Graph Neural Networks for NLP
- GitHub - maziarraissi/Applied-Deep-Learning: Applied Deep Learning
- GitHub - dair-ai/ML-YouTube-Courses: A repository to index and organize the latest machine learning courses found on YouTube.
- GitHub - NVIDIA/DeepLearningExamples: Deep Learning Examples
- GitHub - eugeneyan/applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
- GitHub - tmabraham/awesome-fastai: A curated list of awesome fastai projects/blog posts/tutorials/etc.
- GitHub - booknlp/booknlp: BookNLP, a natural language processing pipeline for books
- GitHub - amitness/learning: Becoming better at data science every day
- GitHub - dair-ai/Transformers-Recipe: A quick recipe to learn all about Transformers
- GitHub - minitorch/minitorch: The full minitorch student suite.
- GitHub - rmcelreath/stat_rethinking_2022: Statistical Rethinking course winter 2022
- GitHub - qdrant/awesome-metric-learning: 😎 A curated list of awesome practical Metric Learning and its applications
- GitHub - kurtispykes/Natural-Language-Processing: Curated articles and code on NLP
- GitHub - kurtispykes/Deep-Learning: Curated articles and code on deep learning topics
- GitHub - lucidrains/DALLE2-pytorch: Implementation of DALL-E 2, OpenAI’s updated text-to-image synthesis neural network, in Pytorch
- GitHub - ivan-bilan/The-NLP-Pandect: A comprehensive reference for all topics related to Natural Language Processing
- GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
- GitHub - anantzoid/VQA-Keras-Visual-Question-Answering: Visual Question Answering task written in Keras that answers questions about images
- GitHub - carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow
- GitHub - ryankiros/skip-thoughts: Sent2Vec encoder and training code from the paper “Skip-Thought Vectors”
- GitHub - btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)
- GitHub - speechbrain/speechbrain: A PyTorch-based Speech Toolkit
- GitHub - The-AI-Summer/learn-deep-learning: AI Summer’s complete catalog of articles
- GitHub - dennybritz/deeplearning-papernotes: Summaries and notes on Deep Learning research papers
- GitHub - Synthaze/EpyNN: Educational python for Neural Networks.
- GitHub - Nyandwi/machine_learning_complete: A comprehensive repository containing 30+ notebooks on learning machine learning!
- GitHub - kenjihiranabe/The-Art-of-Linear-Algebra: Graphic notes on Gilbert Strang’s “Linear Algebra for Everyone”
- GitHub - dair-ai/ML-Notebooks: A series of code examples for all sorts of machine learning tasks and applications.
- GitHub - khuyentran1401/Data-science: Collection of useful data science topics along with code and articles
- GitHub - Ying1123/awesome-neural-symbolic: A list of awesome neural symbolic papers.
- GitHub - CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience
- GitHub - hollance/neural-engine: Everything we actually know about the Apple Neural Engine (ANE)
- https://github.com/karpathy/minGPT
- GitHub - NielsRogge/Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.
- 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.
- GitHub - karpathy/nanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.
- GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML
- GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
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Industry Related
- GitHub - andrewekhalel/MLQuestions: Machine Learning and Computer Vision Engineer - Technical Interview Questions
- 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.
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Interpretability
- A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist
- GitHub - cdpierse/transformers-interpret: Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
- GitHub - g8a9/ferret: A python package for benchmarking interpretability techniques.
- GitHub - neelnanda-io/TransformerLens
- GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.
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MLOps
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Publications and Annotations
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Publications, Annotations and Visualizations
- A Mathematical Framework for Transformer Circuits
- Transformers from Scratch
- The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
- You don’t know JAX
- The Annotated Transformer
- Differentiable Programming from Scratch – Max Slater – Computer Graphics, Programming, and Math
- Logistic Regression
- The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time.
- CS 221 ― Artificial Intelligence - Cheat Sheets
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - PDF
- Attention, Transformers, in Neural Network Large Language Models
- Transformer Circuits
- Transformers from Scratch
- OpenAI Microscope
- A Visual Guide to Vision Transformers
- 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, … 🧠
- The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time.
- The Annotated Transformer
- Annotated S4
- Attention, Transformers, in Neural Network Large Language Models
- LLM Visualization
- The Illustrated AlphaFold
- A Visual Guide to Quantization
- Interactive Tools for machine learning, deep learning, and math
- Smol Training Playbook
- Pandas Tutor - visualize Python pandas code
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Resources
- An introduction to Generative Adversarial Networks (with code in TensorFlow) - AYLIEN News API
- Generative Models
- Adverarial Nets
- Generative Adversarial Nets in TensorFlow - Agustinus Kristiadi
- A (Very) Gentle Introduction to Generative Adversarial Networks (a.k…
- Eric Jang: Generative Adversarial Nets in TensorFlow (Part I)
- Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode | by Arthur Juliani | Medium
- YouTube - Active One-shot Learning
- 1605.06065 One-shot Learning with Memory-Augmented Neural Networks
- Differential neural computer from DeepMind and more advances in backward propagation
- Google’s DeepMind AI Now Capable of ‘Deep Neural Reasoning’ – The New Stack
- Tutorial - What is a variational autoencoder? – Jaan Altosaar
- Variational Autoencoders Explained
- Under the Hood of the Variational Autoencoder (in Prose and Code)
- Variational Autoencoder in TensorFlow
- Eric Jang: Tutorial: Categorical Variational Autoencoders using Gumbel-Softmax
- Implementing Dynamic memory networks · YerevaNN
- Variational Autoencoder (VAE) in Pytorch - Agustinus Kristiadi
- PyTorch quick start: Classifying an image — Outcome Blog documentation
- 1602.05568 Multi-layer Representation Learning for Medical Concepts
- 1605.03481 Tweet2Vec: Character-Based Distributed Representations for Social Media
- 1603.07012 Semi-supervised Word Sense Disambiguation with Neural Models
- 1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
- 1704.08847 Parseval Networks: Improving Robustness to Adversarial Examples
- SoundNet: Learning Sound Representations from Unlabeled Video - MIT
- DeepMoji
- Introduction to Machine Learning Interviews Book · MLIB
- Schedule « AGI-21: SF Bay Area and Virtual, Oct. 15-18, 2021
- Machine Learning Crash Course | Google Developers
- Stanford CRFM
- HuBERT: How to Apply BERT to Speech, Visually Explained | Jonathan Bgn
- Python Numpy Tutorial (with Jupyter and Colab)
- Stanford DAWN · DAWN
- 2022 AGI Safety Fundamentals alignment curriculum
- Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
- Deep Learning Links
- MLExpert | MLExpert - land your dream Machine Learning job
- Cloudera Fast Forward Blog
- Design Patterns in Machine Learning Code and Systems
- The Illustrated Machine Learning Website
- An end to end implementation of a Machine Learning pipelinet
- Schedule « AGI-21: SF Bay Area and Virtual, Oct. 15-18, 2021
- A (Very) Gentle Introduction to Generative Adversarial Networks (a.k…
- Fastcore - Fast.ai
- Stanford DAWN · DAWN
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