Popular GitHub Repositories for Stanford Dogs Dataset

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

pix2tex: Using a ViT to convert images of equations into LaTeX code.

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

Open source annotation tool for machine learning practitioners.

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

Techniques for deep learning with satellite & aerial imagery

BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.

A powerful tool for creating fine-tuning datasets for LLM

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

A Collection of Variational Autoencoders (VAE) in PyTorch.

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Awesome Pretrained Chinese NLP Models，高质量中文预训练模型&大模型&多模态模型&大语言模型集合

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

Data processing for and with foundation models! ? ? ? ?? ??? ? ?

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"