channingping Goto Github PK

02-sentiment-analysis

02-sentiment-analysis-basics

03-data-visualization-basics

for DSW Week 3

argumentclassification

bilibili_scraper

bottom-up-and-top-down-attention-for-image-captioning-using-efficientdet-pytorch

This repository is a pytorch implementation of Bottom-up and Top-down Attention for Image Captioning.

channingping.github.com

channingping.github.io

Herring Cove is a clean and responsive theme for Jekyll.

citation-context-summarization

An algorithm for extractive summarization of citation contexts of a paper based on C-LexRank

cnn-sentence-classification

A tensorflow implementation of Convolutional Neural Networks for Sentence Classification

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

cnn-with-dual-local-and-global-attention

Implementaion of Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction

cnn_sentence

CNNs for sentence classification

comparativesentencesextraction

convsent

The training code for the EMNLP 2017 paper "Learning Generic Sentence Representations Using Convolutional Neural Networks"

cross-domain-sentiment-classification

d2go

D2Go is a toolkit for efficient deep learning

datascienceworkshop-w5

deepconn

This is our implementation of DeepCoNN

deeplearningbook-chinese

Deep Learning Book Chinese Translation

deeplearningcoursecodes

deepwalk

DeepWalk - Deep Learning for Graphs

densitypeakcluster

A cluster framework for 'Clustering by fast search and find of density peaks' in science 2014.

densitypeakcluster-1

Python Code For 'Clustering By Fast Search And Find Of Density Peaks' In Science 2014.

doc2vec

Python scripts for training/testing paragraph vectors

dsw-week4-networkvisualization

emailextractor

entityrecognition

fashion_attgan

A dataset of fashion clothing images paired with attribute labels.

graph_representation_learning_with_gloabal_structural_information

This is a python implementation of the paper "GraRep: Learning Graph Representations with Global Structural Information". The improved implementation takes isolated nodes into consideration, generates distributed representation using Autoencoder instead of factorising matrix with SVD.