Vikram Jha's repositories
BayesDB
A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself
binutils
GNU Binutils ported to support CGC
content
Official content for Harvard CS109
convnetjs
Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
crosscat
A domain-general, Bayesian method for analyzing high-dimensional data tables
data-science-toolbox
Start doing data science in minutes
dataset-examples
Samples for users of the Yelp Academic Dataset
Datumbox-Python-Wrapper
A python wrapper for Datumbox
decaf-release
Decaf is DEPRECATED! Please visit http://caffe.berkeleyvision.org/ for Caffe, the new framework that has all the good things: GPU computation, full train/test scripts, native C++, and an active community!
DeepLearning
My deep learning projects
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.
diveintopython
A mirror of diveintopython.org.
gitbook
Command line utility for generating books and exercises using GitHub/Git and Markdown
golearn
Machine Learning for Go
IJulia.jl
Julia kernel and magics for IPython
jubatus
Framework and Library for Distributed Online Machine Learning
ML_for_Hackers
Code accompanying the book "Machine Learning for Hackers"
nilearn
Machine learning for NeuroImaging in Python
numba
NumPy aware dynamic Python compiler using LLVM
oryx
Simple real-time large-scale machine learning infrastructure.
PredictionIO-Python-SDK
PredictionIO Python SDK
PyCall.jl
Package to call Python functions from the Julia language
pystruct
Simple structured learning framework for python
r-python
Exploring data related to relative usage of R vs. python
rest.li-api-hub
API Hub is a web UI for browsing and searching a catelog of rest.li APIs.
RMOA
Connect R to MOA for massive online data stream mining
stat-learning
Notes and exercise attempts for "An Introduction to Statistical Learning"