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This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
PyTorch implementation of some attentions for Deep Learning Researchers.
A playbook for systematically maximizing the performance of deep learning models.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
Large Language-and-Vision Assistant for Biomedicine, built towards multimodal GPT-4 level capabilities.
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
Source code for Twitter's Recommendation Algorithm
A collection of libraries to optimise AI model performances
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
Neural Networks: Zero to Hero
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
About Code release for "Flowformer: Linearizing Transformers with Conservation Flows" (ICML 2022), https://arxiv.org/pdf/2202.06258.pdf
Source code of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".
🎓 Sharing machine learning course / lecture notes.
Pure python implementation of byte pair encoding (subword tokenization)
Jupyter notebooks for the Natural Language Processing with Transformers book
Best Practices on Recommendation Systems
"Probabilistic Machine Learning" - a book series by Kevin Murphy