rgenaro1's Repositories
rgenaro1/fastText
Library for fast text representation and classification.
rgenaro1/ipython
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
rgenaro1/tensorflow
Computation using data flow graphs for scalable machine learning
rgenaro1/scikit-image
Image Processing SciKit (Toolbox for SciPy)
rgenaro1/Paddle
PArallel Distributed Deep LEarning
rgenaro1/scikit-learn
scikit-learn: machine learning in Python
rgenaro1/numpy
Numpy main repository
rgenaro1/Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
rgenaro1/pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
rgenaro1/scipy
Scipy library main repository
rgenaro1/math-ml
rgenaro1/matplotlib-gallery
Examples of matplotlib codes and plots
rgenaro1/models
Models built with TensorFlow
rgenaro1/tutorials
A series of machine learning tutorials for Torch7
rgenaro1/useR-machine-learning-tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
rgenaro1/GraphLab-Create-SDK
SDK for Turi's GraphLab Create.
rgenaro1/SFrame
SFrame: Scalable tabular and graph data-structures built for out-of-core data analysis and machine learning.
rgenaro1/mlbooks
Collection of free machine learning books
rgenaro1/latex-templates
Website for personal collection and previewing of LaTeX templates. Presented with Python/Jinja2.
rgenaro1/caffe
Caffe: a fast open framework for deep learning.
rgenaro1/NoteBooks-Statistics-and-MachineLearning
http://unsupervised-learning.com
rgenaro1/cookbook-code
Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python
rgenaro1/sklearn_scipy2013
Scikit-learn tutorials for the Scipy 2013 conference