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deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

edward

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

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

gpflow

Gaussian processes in TensorFlow

harris_corners

The script evaluates Harris-Stephens corner points for a give RGB or grayscale image

hough_transform

Detecting line and circle parameters via Hough Transformation

imbalanced-learn

Python module to perform under sampling and over sampling with various techniques.

impute

This is a read-only mirror of the Bioconductor SVN repository. Package Homepage: http://bioconductor.org/packages/devel/bioc/html/impute.html Bug Reports: https://support.bioconductor.org/p/new/post/?tag_val=impute.

imputemachine

Missing Data Imputation with Machine Learning Models and Algorithms

jupyterlab_dstools

missforest

missForest is a nonparametric, mixed-type imputation method for basically any type of data for the statistical software R.

mlpack

mlpack: a scalable C++ machine learning library --

mlr

mlr: Machine Learning in R

models

Models built with TensorFlow

numpy

Numpy main repository

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

pyro

Deep universal probabilistic programming with Python and PyTorch

python-scraping

Code samples from the book Web Scraping with Python http://shop.oreilly.com/product/0636920034391.do

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

ray

A high-performance distributed execution engine

scikit-learn

scikit-learn: machine learning in Python

scipy

Scipy library main repository

tensorflow

Computation using data flow graphs for scalable machine learning

vim

This package introduces new tools for the visualization of missing and/or imputed values, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missings and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods.