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machine_reading_comprehension

machine reading comprehension with deep learning

machine_translation_en2de

Machine Translation system translates Text from english to german.

match-lstm

A PyTorch implemention of Match-LSTM, R-NET and M-Reader for Machine Reading Comprehension

memn2n

End-To-End Memory Network using Tensorflow

memn2n-tensorflow

"End-To-End Memory Networks" in Tensorflow

memn2n-tensorflow-1

Implementation of End-To-End Memory Networks with Tensorflow for bAbI Dataset

memnn

Memory Networks implementations

neural-qa

QA model trained on the SQuAD dataset.

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

nmt

TensorFlow Neural Machine Translation Tutorial

ntlk_qa

Question_Answering_NTLK

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

qa-deep-learning-stanford-squad

Question-Answering Stanford's SQuAD dataset using deep learning.

qa-with-tensorflow

qanet

A Tensorflow implementation of QANet for machine reading comprehension

question-answering

TensorFlow implementation of Match-LSTM and Answer pointer for the popular SQuAD dataset.

questionanswering-dynamicmemorynetworks

A QA system based on Dynamic Memory Networks

r-net

A Tensorflow Implementation of R-net: Machine reading comprehension with self matching networks

rdpy

Remote Desktop Protocol in Twisted Python

reading-comprehension

This contains three different models for reading comprehension task set up to train on SQuAD

reading-comprehension-repo

is the ability to process text, understand its meaning, and to integrate it with what the reader already knows.[1][2] Fundamental skills required in efficient reading comprehension are knowing meaning of words, ability to understand meaning of a word from discourse context, ability to follow organization of passage and to identify antecedents and references in it, ability to draw inferences from a passage about its contents, ability to identify the main thought of a passage and ability to answer questions answered in a passage.

reading_comprehension-cs224n

Simple baseline of reading comprehension task on SQuAD dataset. Most of the it are reimplementation of the ICLR2017 paper "Machine Comprehension Using Match-LSTM and Answer Pointer"

rnn_sent

Recurrent neural networks and Dynamic memory networks for sentiment classification

sentence-selection

Sentence Selection for Reading Comprehension task on the SQuaD question answering dataset

seq2seq_nmt

seqmatchseq

springbootwebapplicationstepbystep

Develop your first web application with Spring Boot Magic

sql_zoo

squad

squad-comprehension-net

Reading comprehension model for Stanford Question Answer Database using deep learning