dukeyuan's repositories
twitter-sentiment-analysis
Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
WideDeepLearning
A general implementation of linear/dnn/wide&deep learning model
ffm_mpi
LR、FM model solved by ftrl and sgd parallel on MPI
dlib
A toolkit for making real world machine learning and data analysis applications in C++
ChatBotCourse
自己动手做聊天机器人教程
rnn-text-classification
A text classification model based on RNN(recurrent neural network)
lda
Topic modeling with latent Dirichlet allocation using Gibbs sampling
word2vec
Python interface to Google word2vec
Difacto_DMLC
Distributed FM and LR based on Parameter Server with Ftrl
FreeProgrammingBooksCN
免费的计算机编程类中文书籍，欢迎投稿
ftrl_proximal_lr
Multithreaded Asynchronous FTRL Proximal Implementation
alphaFM
Multi-thread implementation of Factorization Machines with FTRL for binary-class classification problem.
SparkLearning
Learning Apache spark,including code and data .Most part can run local.
tensorflow_novelist
模仿莎士比亚创作戏剧！屌炸天的是还能创作金庸武侠小说！快star，保持更新！！
Advanced-Factorization-of-Machine-Systems
GSOC 2017 - Apache Organization - # Implementation of Factorization Machines on Spark using parallel stochastic gradient descent (python and scala)
wormhole
Deprecated
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
dmlc-core
A common bricks library for building scalable and portable distributed machine learning.
pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
libffm-ftrl
libffm with ftrl updater
DeepLearning-MXNet
Mxnet for CTR
TopDeepLearning
A list of popular github projects related to deep learning
jieba
结巴中文分词
tiny-dnn
header only, dependency-free deep learning framework in C++11
ffm
update the libffm-1.13. Add FTRL and linear
stopwords-json
Stopwords for 50 languages in JSON format
scikit-learn
scikit-learn: machine learning in Python
DeepLearnToolbox
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.