Popular GitHub Repositories for Stanford Openie

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

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

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

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

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

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

All notes and materials for the CS229: Machine Learning course by Stanford University

The "official" unofficial DSPy framework. Build LLM powered agents and other workflows, based on the Stanford DSP paper.

Rigorously test, monitor, and optimize agent systems—grounded in research from Stanford and Berkeley AI Labs.

? Use the latest Stanza (StanfordNLP) research models directly in spaCy

Stanford Open Information Extraction made simple!

Một cuốn sách về Học Sâu đề cập đến nhiều framework phổ biến, được sử dụng trên 300 trường Đại học từ 55 đất nước bao gồm MIT, Stanford, Harvard, và Cambridge.

Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.

A curated list of Open Information Extraction (OIE) resources: papers, code, data, etc.

Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.

Simple Implementation of many GAN models with PyTorch.

Complete Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.

NLP Capabilities in Neo4j

Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera