siddbanpsu Goto Github PK

abtextsumm

Summarization code

academic-kickstart

boilerpipe

Work in progress transmit from Google Code

cicd-python-heroku

Python Web app and Heroku CI/CD.

cvnd

Projects from Computer Vision Nanodegree on Udacity

ddw_kmeansclustering

deep-text-classification-pytorch

Deep neural network text classification papers in PyTorch

deep_learning_notebooks

Pytorch Jupyter Notebooks for different applications

deepgraphcnnfortexts

incubator-mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

keras

Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.

keras-io

Keras documentation, hosted live at keras.io

mxnet-the-straight-dope

An interactive book on deep learning. Much easy, so MXNet. Wow.

neural-aspect-extraction-airbnb

Unsupervised aspect extraction on airbnb reviews

paraphrasing

Rewriting based on Integer Linear Programming formulation

potara

Multi-document summarization tool relying on ILP and sentence fusion

production-tools

A bare-bones repository demonstrating how to set up tools for data science projects that will help you write higher quality code.

pyrouge

An interface to and, in time, a Python reimplementation of the ROUGE package for evaluating summarization

pytorch-qrnn

PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

pytorch_tutorial

PyTorch tutorial for PyData Berlin

semantic_summ

semantic summarization using abstract meaning representation (AMR)

sequence-to-sequence-generation-skip-thoughts-

Modeling the Skip Thoughts Paper http://arxiv.org/pdf/1506.06726v1.pdf in Keras

staticman

💪 Static sites with superpowers

wikikreator

ACL paper source code for Wikipedia stub content generator

word2vecpy

Python implementation of CBOW and skip-gram word vector models, and hierarchical softmax and negative sampling learning algorithms