yjcelly

kaldi

This is now the official location of the Kaldi project.

data_science_final_capstone

clean_data_project_wearable

Linear_Regression_project

Developing_Data_Products_Presentation

Developing_Data_Products_shiny

Practical_ML_writeup_week3

the week3 project for Practical Machine Learning

attention-lvcsr

End-to-End Attention-Based Large Vocabulary Speech Recognition

RepData_PeerAssessment1

Peer Assessment 1 for Reproducible Research

ExData_Plotting1

Plotting Assignment 1 for Exploratory Data Analysis

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

ProgrammingAssignment2

Repository for Programming Assignment 2 for R Programming on Coursera

clean_data_week_1

datasciencecoursera

the empty repo for data science of coursera

datasharing

The Leek group guide to data sharing

test_data_mine

a simple test repo

minerva

Minerva: a fast and flexible tool for deep learning. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy. Please refer to the examples to see how multi-GPU setting is used.

kaldi-lstm

C++ implementation of LSTM (Long Short Term Memory), in Kaldi's nnet1 framework. Used for automatic speech recognition, possibly language modeling etc, the training can be switched between CPU and GPU(CUDA).

parameter_server

A distributed machine learning framework.

third_party

depended libraries of parameter server

SparkInternals

Notes talking about the design and implementation of Apache Spark

parallelismBook

spark-jobserver

REST job server for Spark

spark

Mirror of Apache Spark

pylearn2

A Machine Learning library based on Theano

kaggle-galaxies

Winning solution for the Galaxy Challenge on Kaggle (http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge)

tiny-cnn

convolutional neural networks in C++11/TBB

test

the first github test

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.

cmusphinx

A git clone of the CMU Sphinx svn repository