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Master programming by recreating your favorite technologies from scratch.
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Overview and tutorial of the LangChain Library
research work on multimodal cognitive ai
A playbook for systematically maximizing the performance of deep learning models.
CoreNet: A library for training deep neural networks
OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
Awesome-LLM: a curated list of Large Language Model
Making large AI models cheaper, faster and more accessible
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
Jupyter notebooks for the Natural Language Processing with Transformers book
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and mo…
A Python package to assess and improve fairness of machine learning models.
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
Mastering PyTorch, published by Packt
Python books free to read online or download
A game theoretic approach to explain the output of any machine learning model.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Source code for Witvliet et al., 2021, Nature
[NAACL 2021] Factual Probing Is [MASK]: Learning vs. Learning to Recall https://arxiv.org/abs/2104.05240