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models-1

Model zoo for genomics

motifraptor

Explore the effect of genetic variants on transcription factor binding sites

muzero-general

MuZero

netron

Visualizer for neural network, deep learning and machine learning models

nn

onnx

Open Neural Network Exchange

openai-cookbook

Examples and guides for using the OpenAI API

opendata

开源的金融投资数据提取工具，专注在各类网站上爬取数据，并通过简单易用的API方式使用

plotmi

Visualize intearactions learned by a machine learning model by plotting mutual information between pairwise k-mer distributions

plotneuralnet

Latex code for making neural networks diagrams

plugins-quickstart

Get a ChatGPT plugin up and running in under 5 minutes!

polya

Analysis of TEX/5PD/3PD RNA-seq

pyomo

An object-oriented algebraic modeling language in Python for structured optimization problems.

pyro

Deep universal probabilistic programming with Python and PyTorch

pysc2

StarCraft II Learning Environment

pytdx

Python tdx数据接口

python-mip

Collection of Python tools for the modeling and solution of Mixed-Integer Linear programs

pyvenn

2 ~ 6 sets venn diagram for python

random-network-distillation

Code for the paper "Exploration by Random Network Distillation"

ray

An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

rllab

rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.

saida

scvi

Deep generative modeling for single-cell omics data

self-attention-gan

sentence-transformers

Multilingual Sentence & Image Embeddings with BERT

sentencepiece

Unsupervised text tokenizer for Neural Network-based text generation.

shap

A unified approach to explain the output of any machine learning model.

simdna

Simulated datasets of DNA

singan

Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"