ai-links
github.com/khushmeeet/ai-links ↗This is the list of links on Deep Learning that I have collected over time and still collecting.
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me resources resources from ai-links"
Installation instructions →What's inside
Resources
- 1602.05568 Multi-layer Representation Learning for Medical Concepts
- 1603.07012 Semi-supervised Word Sense Disambiguation with Neural Models
- 1605.03481 Tweet2Vec: Character-Based Distributed Representations for Social Media
- 1605.06065 One-shot Learning with Memory-Augmented Neural Networks
- 1704.08847 Parseval Networks: Improving Robustness to Adversarial Examples
- 1708.00524 Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Interpretability
- A Comprehensive Mechanistic Interpretability Explainer & Glossary - Dynalist
Dynalist
- GitHub - cdpierse/transformers-interpret: Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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.
g8a9/ferret: A python package for benchmarking interpretability techniques.
- GitHub - neelnanda-io/TransformerLens
neelnanda-io/TransformerLens
- GitHub - slundberg/shap: A game theoretic approach to explain the output of any machine learning model.
slundberg/shap: A game theoretic approach to explain the output of any machine learning model.
Publications, Annotations and Visualizations
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning - PDF
PDF
- A Mathematical Framework for Transformer Circuits
- Annotated S4
- Attention, Transformers, in Neural Network Large Language Models
- A Visual Guide to Quantization
- A Visual Guide to Vision Transformers
Courses
- AMMI Geometric Deep Learning Course - Second Edition (2022) - YouTube
Second Edition (2022) - YouTube
- A visual introduction to machine learning
- Cornell CS4780 - Machine Learning for Intelligent Systems
Machine Learning for Intelligent Systems
- “Crash Course” - ML@B Blog Berkeley
ML@B Blog Berkeley
- Deep Learning for Natural Language Processing
- Deep Learning for Particle Physicists — Deep Learning for Particle Physicists
Deep Learning Repositories
- GitHub - amitness/learning: Becoming better at data science every day
amitness/learning: Becoming better at data science every day
- GitHub - anantzoid/VQA-Keras-Visual-Question-Answering: Visual Question Answering task written in Keras that answers questions about images
anantzoid/VQA-Keras-Visual-Question-Answering: Visual Question Answering task written in Keras that answers questions about images
- GitHub - booknlp/booknlp: BookNLP, a natural language processing pipeline for books
booknlp/booknlp: BookNLP, a natural language processing pipeline for books
- GitHub - btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)
btcsuite/btcd: An alternative full node bitcoin implementation written in Go (golang)
- GitHub - carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow
carpedm20/MemN2N-tensorflow: “End-To-End Memory Networks” in Tensorflow
- GitHub - CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience
CYHSM/awesome-neuro-ai-papers: Papers from the intersection of deep learning and neuroscience
Industry Related
- GitHub - andrewekhalel/MLQuestions: Machine Learning and Computer Vision Engineer - Technical Interview Questions
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.
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.
MLOps
- GitHub - dair-ai/MLOPs-Primer: A collection of resources to learn about MLOPs.
dair-ai/MLOPs-Primer: A collection of resources to learn about MLOPs.
Showing a sample of 143 resources. View the full list on GitHub →