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xxyy1's Projects

dlrm icon dlrm

An implementation of a deep learning recommendation model (DLRM)

dnn_ctr icon dnn_ctr

The framework to deal with ctr problem。The project contains FNN,PNN,DEEPFM, NFM etc

dopamine icon dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

drl4recsys icon drl4recsys

Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems

esl-cn icon esl-cn

The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。

facets icon facets

Visualizations for machine learning datasets

feature-engineering-book icon feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

feature-selector icon feature-selector

Feature selector is a tool for dimensionality reduction of machine learning datasets

free-api icon free-api

收集免费的接口服务,做一个api的搬运工

gbdt icon gbdt

Gradient boosting decision trees.

graphvite icon graphvite

A general and high-performance graph embedding system for various applications

handson-ml icon handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

icme2019-ctr icon icme2019-ctr

The Code for ICME2019 Grand Challenge: Short Video Understanding (Single Model Ranks 6th)

interview_internal_reference icon interview_internal_reference

2019年最新总结,阿里,腾讯,百度,美团,头条等技术面试题目,以及答案,专家出题人分析汇总。

jarvis icon jarvis

Jarvis is a toolbox built on top of TensorFlow2.0 that allows developers and researchers to easily build neural networks in TensorFlow, particularly CTR models for large-scale advertising and recommendation scenarios.

jd icon jd

JData京东算法大赛入门程序

jdata icon jdata

京东JData算法大赛-高潜用户购买意向预测入门程序(starter code)

kaggle icon kaggle

Kaggle 项目实战(教程) = 文档 + 代码 + 视频

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