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RAG (Retrieval-Augmented Generation) Chatbot Examples Using PyMuPDF
Time Series Analysis and Forecasting in Python
This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
Probabilistic programming with HuggingFace language models
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Data Engineering Zoomcamp is a free nine-week course that covers the fundamentals of data engineering.
We write your reusable computer vision tools. 💜
The best repository showing why transformers might not be the answer for time series forecasting and showcasing the best SOTA non transformer models.
Educational Transformer from scratch (no autograd), with forward and backprop.
A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.
Predict time-series with one line of code.
Understanding Deep Learning - Simon J.D. Prince
This is the official release code of AAAI2023 accepted paper: "Causal Conditional Hidden Markov Model for Multimodal Traffic Prediction"
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
List of Molecular and Material design using Generative AI and Deep Learning
Example code from the book "Deep Learning for the Life Sciences"
Learn ML engineering for free in 4 months!
Free MLOps course from DataTalks.Club
High-quality Neural Networks for Computer Vision 😎
Curated list of resources for variant prioritization
jQuery plugin for highlighting bits of text within a textarea.
A modular, reusable university course for Rust
Code for "Towards Explainable Multi-modal Motion Prediction using Graph Representations"