Stars
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
This repository contains implementations and illustrative code to accompany DeepMind publications
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
This repository contains demos I made with the Transformers library by HuggingFace.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SL…
Biological foundation modeling from molecular to genome scale
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.
Code for the ProteinMPNN paper
Diffusion models of protein structure; trigonometry and attention are all you need!
Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design
Implementation of "Large Steps in Inverse Rendering of Geometry"
Evolutionary couplings from protein and RNA sequence alignments
Tutorials for Machine Learning on Graphs
Protein-ligand structure prediction
CLASP - Contrastive Language-Aminoacid Sequence Pretraining
Stanford University Rad229 Class Code: MRI Signals and Sequences
Protein language model customized for antibodies
Unconstrained lattice antibody-antigen bindings generator
Minimal package to demonstrate good Python development habits