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acl2020-openqa-tutorial

ACL2020 Tutorial: Open-Domain Question Answering

ajax-ws

ajax related explorations

algorithms-clrs

aml-book

awesome-mlops

A curated list of references for MLOps

awesome-multimodal-ml

Reading list for research topics in multimodal machine learning

awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

awesome-system-design-resources

This repository contains System Design resources which are useful while preparing for interviews and learning Distributed Systems

axlearn

bert

TensorFlow code and pre-trained models for BERT

brown-cluster

C++ implementation of the Brown word clustering algorithm.

bumptop

Automatically exported from code.google.com/p/bumptop

canvas_sdk

Javascript SDK for Force.com Canvas

cayley

An open-source graph database

cherryblossom

conceptnet5

Code for building ConceptNet from raw data.

d2l.ai

Berkeley Stat 157 - Deep Learning course

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deepdream

deeplearningframeworks

Demo of running NNs across different frameworks

deeplearningstars

Top Stared Deep Learning Projects

dpi_hf_datasets

repo for creating readme/files for DPI HF datasets

drqa

DrQA repo

finetune-transformer-lm

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"

flan

gap-coreference

GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of (ambiguous pronoun, antecedent name), sampled from Wikipedia for the evaluation of coreference resolution in practical applications.

generative-ai-for-beginners

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

glossextractionengine

Natural Language Processing framework written in python to extract definitional sentences about real world named entities from large datasets like Wikipedia and ClueWeb. The core of the framework is based on the filters, transformations, parsers, feature extractors, samplers and modelers you use. Thus it is extensible and customizable for your needs. All you need to do is extend the base functionality and write your own filters, transformations, parsers, feature extractors, samplers and modelers specific for your NLP task or datasource.

grokking-deep-learning

this repository accompanies the book "Grokking Deep Learning"

gt-nlp-class

Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"