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CTRmodel

CTR prediction model based on spark(LR, GBDT, DNN)

Language:ScalaApache-2.0000

WordMultiSenseDisambiguation

WordMultiSenseDisambiguation, chinese multi-wordsense disambiguation based on online bake knowledge base and semantic embedding similarity compute,基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧.

Language:Python000

CrimeKgAssitant

Crime assistant including crime type prediction and crime consult service based on nlp methods and crime kg,罪名法务智能项目,内容包括856项罪名知识图谱, 基于280万罪名训练库的罪名预测,基于20W法务问答对的13类问题分类与法律资讯问答功能.

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ecosystem

Integration of TensorFlow with other open-source frameworks

Language:ScalaApache-2.0000

recomendation-system

Design & Development of a Recommendation System for Goodreads & Development of a Multi-Label Classification Model from textual data using Deep Learning

Language:Jupyter Notebook000

attention-ocr

A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine.

Language:PythonMIT000

Chinese-Word-Vectors

100+ Chinese Word Vectors 上百种预训练中文词向量

Language:PythonApache-2.0000

DKN

A tensorflow implementation of DKN (Deep Knowledge-aware Network for News Recommendation)

Language:C++000

Neural-Collaborative-Filtering-approach-for-Movie-Recommendation-System

This program implements Recommendation system using Neural Collaborative Filtering which uses a Deep learning for collaborative filtering

Language:PythonApache-2.0000

Chinese-sentiment-analysis-with-Doc2Vec

using jieba and doc2vec to implement sentiment analysis for Chinese docs

Language:Python000

Recommender-System-for-Movies-using-Boltzmann-Machine

From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. I work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender. Our f model Deep Belief Networks, complex Boltzmann Machines that will be covered for recommender system. The list of movies will be explicit so simply need to rate the movies you already watched, input your ratings in the dataset, execute model and voila! The Recommender System tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!

Language:Jupyter Notebook100

DeepRec

Implementation of Deep Learning based Recommender Algorithms with Tensorflow.

Language:PythonGPL-3.0000

Attention-OCR-1

Visual Attention based OCR

Language:PythonMIT000

sequence-labeling-BiLSTM-CRF

BiLSTM-CRF joint model for tasks like sequence labeling using char/word level embeddings in Tensorflow

Language:PythonGPL-3.0000

cdmf

Hicss best paper nominated: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

Language:PythonBSD-2-Clause100

xDeepFM

Language:Python000

abtest-sdk

ABTest-SDK适用于AB分流实验、灰度发布系统等互联网场景；此SDK支持二次开发，开发者可自行实现查询实验分流配置接口。特性： 1）支持白名单； 2）轻量级，仅依赖spring、guava； 3）接口耗时微秒级，使用guava的本地缓存； 4）客户分流可控（修改分流比时候客户版本可控，单个实验的分流比例粒度为5%）；

Language:Java000