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algorithms

Algorithms & Data Structures in C++

allennlp

An open-source NLP research library, built on PyTorch.

aoareader

PyTorch implementation of Attention-over-Attention Neural Networks for Reading Comprehension

attention-over-attention

Implementation of Attention-over-Attention Neural Networks for Reading Comprehension (https://arxiv.org/abs/1607.04423) in TensorFlow

autodidact

A pedagogical implementation of Autograd

awaresome-neural-models-for-semantic-match

A curated list of papers dedicated to neural text (semantic) matching.

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

awesome-question-answering

Resources, datasets, papers on Question Answering

bert

TensorFlow code and pre-trained models for BERT

bert-n-pals

Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning (https://arxiv.org/abs/1902.02671)

bert-pytorch

Google AI 2018 BERT pytorch implementation

big-data-resources

:boom:大数据/数据挖掘/推荐系统/机器学习相关资源

bimpm-pytorch

Re-implementation of BIMPM(Bilateral Multi-Perspective Matching for Natural Language Sentences, Zhiguo Wang et al.) on Pytorch.

c-algorithms

A library of common data structures and algorithms written in C.

caffe

Caffe: a fast open framework for deep learning.

cda-cikm2020

chinese-poetry

最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。

classification_zoo

toolkit of all kinds of deep classification models

code_and_data

包含数据和代码

codeforce

code force code

conference_paper2017

coursera-machine-learning

Coursera Machine Learning - Python code

crawler4j

Open Source Web Crawler for Java

cs231n

cs231n homework

data

data_repo

deep-siamese-text-similarity

Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character embeddings

deeplearningzerotoall

TensorFlow Basic Tutorial Labs

dl4marco-bert

drqa

A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.