AI

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

486102PythonMIT

archai

Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.

48493PythonMIT

PaperRobot

Code for PaperRobot: Incremental Draft Generation of Scientific Ideas

482131PythonMIT

shell-genie

Your wishes are my commands

48239PythonMIT

awesome-nlg

A curated list of resources dedicated to Natural Language Generation (NLG)

48059CC0-1.0

qtrader

Reinforcement Learning for Portfolio Management

480168Jupyter NotebookApache-2.0

visual-qa

[Reimplementation Antol et al 2015] Keras-based LSTM/CNN models for Visual Question Answering

479183PythonMIT

ShenNong-TCM-LLM

Repo for ShenNong-TCM-LLM (“神农”大模型，首个中医药中文大模型)

47871Python

TResNet

Official Pytorch Implementation of "TResNet: High-Performance GPU-Dedicated Architecture" (WACV 2021)

47862PythonApache-2.0

pynlpl

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

47666PythonGPL-3.0