kkk's repositories

ctr_pipeline

通过对于现有开源分布式机器学习工具的整合(主要是基于参数服务器的logistic regression,xgboost,FFM,FM ),打造一个工业级的,可以线上使用的点击率预估流水线

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neural_factorization_machine

TenforFlow Implementation of Neural Factorization Machine

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tensorflow-DeepFM

Tensorflow implementation of DeepFM for CTR prediction.

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fastText

Library for fast text representation and classification.

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记录对于包括互联网在内的技术应用到教育中,以及从教育到产业应用的一些调查和设想

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awesome-ml-for-cybersecurity

:octocat: Machine Learning for Cyber Security

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ctr_pipeline-1

make the ctr(click through rate prediction) pipeline as a service . Prepare the training and testing data as the ordered format and run the pipeline,you can get the model to be used in online ctr prediction

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deep-ctr

An attempt of training DNN models to predict ad click-through rate, implemented with Theano.

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deep_learning

Basic Deep Learning

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DKN

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

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EasyML

Easy Machine Learning

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education_technique

记录对于包括互联网在内的技术应用到教育中,以及从教育到产业应用的一些调查和设想

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fast_tffm

fast_tffm: Tensorflow-based Distributed Factorization Machine

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keras

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

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lc-all-solutions

My own lc solutions

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models

Models built with TensorFlow

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noscope

Accelerating network inference over video

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OpenLearning4DeepRecsys

Some deep learning based recsys for open learning.

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pdf.js

PDF Reader in JavaScript

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simplified-deeplearning

Simplified implementations of deep learning related works

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spark-summit-2017-Europe

Spark summit 2017 europe ppt下载

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tensorflow

Computation using data flow graphs for scalable machine learning

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tensorflow_demo

some demos about tensorflow

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tf-stanford-tutorials

This repository contains code examples for the course CS 20SI: TensorFlow for Deep Learning Research.

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WebIDE

Coding WebIDE Community Edition

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xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

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