Pinned Repositories
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
awesome-2vec
Curated list of 2vec-type embedding models
cosine-lsh-join-spark
Approximate Nearest Neighbors in Spark
d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
deep-recommender-system
深度学习在推荐系统中的应用及论文小结。
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
kaggle-criteo
Kaggle Criteo https://www.kaggle.com/c/criteo-display-ad-challenge
Kaggle-Ensemble-Guide
Code for the Kaggle Ensembling Guide Article on MLWave
Kaggle_The_Hunt_for_Prohibited_Content
Code for Kaggle's The Hunt for Prohibited Content Competition
whos's Repositories
whos/Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
whos/awesome-2vec
Curated list of 2vec-type embedding models
whos/cosine-lsh-join-spark
Approximate Nearest Neighbors in Spark
whos/d2l-zh
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
whos/deep-recommender-system
深度学习在推荐系统中的应用及论文小结。
whos/DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
whos/Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
whos/kaggle-criteo
Kaggle Criteo https://www.kaggle.com/c/criteo-display-ad-challenge
whos/Kaggle-Ensemble-Guide
Code for the Kaggle Ensembling Guide Article on MLWave
whos/Kaggle_The_Hunt_for_Prohibited_Content
Code for Kaggle's The Hunt for Prohibited Content Competition
whos/outbrain-click-prediction-kaggle
Solution to the Outbrain Click Prediction competition
whos/Qix
Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang
whos/spark-ml-source-analysis
spark ml 算法原理剖析以及具体的源码实现分析
whos/state-of-the-art-result-for-machine-learning-problems
This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
whos/UMD-courses
Course homepages for courses that I've taught at the University of Maryland
whos/use_vim_as_ide
use vim as IDE
whos/viz
Interactive visualizations and stats of GitHub's newest, most popular repos. http://www.donnemartin.com/viz
whos/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