reichenbch Goto Github PK

aaai2019-coala-cqa-answer-selection

abcnn

Implementation of ABCNN(Attention-Based Convolutional Neural Network) on Tensorflow

air-traffic-control

Java Centric Air Traffic Control System

android-swp

answer-selection

Code for IWCS 2017 paper "Representation Learning for Answer Selection with LSTM-Based Importance Weighting"

applet1

Implementation of a java applet with glassfish server

covid-xray_classification

dan

Deep Averaging Networks

dataset-sts

Semantic Text Similarity Dataset Hub

deep-reinforcement-learning-for-dialogue-generation-in-tensorflow

Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow

deep-reinforcement-learning-papers

A list of recent papers regarding deep reinforcement learning

deeplearn

Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.

django-polling-app

Django based demo Polling App

eruditex

A Comprehension based Question Answering System

gdown.pl

Google Drive direct download of big files

hearing-aid-matlab

Speech Processing System (Implemented in Matlab)

howto

This repository houses various algorithms, techniques related to Machine Learning and how to work on these algorithms and play with them.

igraphly

Implementation of Graph based algorithms with tutorials.

itws-3

Water Management Project

keyword-extraction-datasets

keywordextractor-datasets

language

Shared repository for open-sourced projects from the Google AI Language team.

lectures

Oxford Deep NLP 2017 course

legalfruitsl

lz77

Implementation of the LZ77 compression algorithm in Python

medicaldiagnosisbot

model-pruning

Code for Model Pruning on MNIST Dataset

msmarco-conversational-search

Truly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.

neural-machine-translation

Implementing neural machine translation from scratch using Python,Keras.

nlp-challenges