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amc

Code for KDD 2014 paper "Mining Topics in Documents: Standing on the Shoulders of Big Data"

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. By everyone, for everyone!

berp-trans

Transcripts for the audio files in the Berkeley Restaurant Project (BeRP) corpus

boringssl

Custom version of Boring SSL used by Mono

boringssl-1

Mirror of BoringSSL

colah.github.io

corenlp

Stanford CoreNLP: A Java suite of core NLP tools.

cryptography

cryptography is a package designed to expose cryptographic primitives and recipes to Python developers.

cryptonets

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

deeplearning-papernotes

Summaries and notes on Deep Learning research papers

dica

Rethinking LDA: moment matching for discrete ICA

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

hdp-faster

Hierarchical Dirichlet Process (with Split-Merge Operations), originally by Chong Wang

jieba

结巴中文分词

ltm

Code for ICML 2014 paper "Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data"

machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

machinelearning

This project contain some machine learning algrithm demo.Maybe the code is also useful to you.

malb.github.io

website

mallet

My fork of MAchine Learning for LanguagE Toolkit

mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, and more

neural-network-papers

nlp-lang

这个项目是一个基本包.封装了大多数nlp项目中常用工具

nltk

NLTK Source

pydelicious

Automatically exported from code.google.com/p/pydelicious

pyltp

pyltp: the python extension for LTP

pystanforddependencies

Python interface for converting Penn Treebank trees to Stanford Dependencies and Universal Depenencies

sage

Mirror of the Sage source tree -- please do not submit PRs here -- everything must be submitted via https://trac.sagemath.org/

show-me-the-code

Python 练习册，每天一个小程序

tensorflow

Computation using data flow graphs for scalable machine learning