20 projects found that use python as the programming language.
Showing Items 1-20 of 104 on page 1 of 6: 1 2 3 4 5 6 Next

Logo Optunity 1.1.0

by claesenm - July 19, 2015, 12:23:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2449 views, 627 downloads, 2 subscriptions

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.

Changes:

The following features have been added:

  • new solvers
  • tree of Parzen estimators (requires Hyperopt)
  • Sobol sequences
  • Octave wrapper
  • support for structured search spaces, which can be nested
  • improved cross-validation routines to return more detailed results
  • most Python examples are now available as notebooks

Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40531 views, 7516 downloads, 4 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.


Logo python weka wrapper 0.3.2

by fracpete - June 28, 2015, 23:09:13 CET [ Project Homepage BibTeX Download ] 14555 views, 3073 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • The "packages" parameter of the "weka.core.jvm.start()" function can be used for specifying an alternative Weka home directory now as well
  • added "train_test_split" method to "weka.core.Instances" class to easily create train/test splits
  • "evaluate_train_test_split" method of "weka.classifiers.Evaluation" class now uses the "train_test_split" method

Logo Recur 1

by douglasbagnall - June 16, 2015, 12:06:05 CET [ Project Homepage BibTeX Download ] 677 views, 153 downloads, 2 subscriptions

About: Recur is a collection of Gstreamer plugins and language modelling tools based on recurrent neural networks.

Changes:

Initial Announcement on mloss.org.


Logo BayesPy 0.3.5

by jluttine - June 9, 2015, 13:17:00 CET [ Project Homepage BibTeX Download ] 6500 views, 1624 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Fix indexing bug in VB optimization (not VB-EM)
  • Fix demos

Logo JMLR dlib ml 18.16

by davis685 - June 4, 2015, 04:50:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 106563 views, 17980 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds a tool for solving linear model predictive control problems as well as improved python bindings and other usability improvements.


Logo ClusterEval 1.1

by cdevries - May 18, 2015, 22:01:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3148 views, 808 downloads, 2 subscriptions

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Changes:

Moved project to GitHub.


Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 7998 views, 1578 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:
  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version


Logo Nilearn 0.1.2

by goulagman - April 29, 2015, 16:16:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 919 views, 220 downloads, 3 subscriptions

About: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Changes:

Initial Announcement on mloss.org.


Logo Blocks 0.1

by bartvm - March 30, 2015, 22:25:02 CET [ Project Homepage BibTeX Download ] 773 views, 208 downloads, 3 subscriptions

About: A Theano framework for building and training neural networks

Changes:

Initial Announcement on mloss.org.


Logo Theano 0.7

by jaberg - March 27, 2015, 16:40:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17693 views, 3241 downloads, 3 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.7 (26th of March, 2015)

We recommend to everyone to upgrade to this version.

Highlights:

* Integration of CuDNN for 2D convolutions and pooling on supported GPUs
* Too many optimizations and new features to count
* Various fixes and improvements to scan
* Better support for GPU on Windows
* On Mac OS X, clang is used by default
* Many crash fixes
* Some bug fixes as well

Logo Loom 0.2.10

by fritzo - March 19, 2015, 19:22:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 897 views, 193 downloads, 2 subscriptions

About: A streaming inference and query engine for the Cross-Categorization model of tabular data.

Changes:

Initial Announcement on mloss.org.


Logo apsis 0.1.1

by fdiehl - March 17, 2015, 08:27:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 931 views, 206 downloads, 2 subscriptions

About: A toolkit for hyperparameter optimization for machine learning algorithms.

Changes:

Initial Announcement on mloss.org.


Logo libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 5602 views, 1172 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:

This is a major release, with several novelties, improvements and fixes, among which:

  • step-size two-point adaptaion scheme for improved performances in some settings, ref #88

  • important bug fixes to the ACM surrogate scheme, ref #57, #106

  • simple high-level workflow under Python, ref #116

  • improved performances in high dimensions, ref #97

  • improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

  • elitist mechanism for forcing best solutions during evolution, ref 103

  • new legacy plotting function, ref #110

  • optional initial function value, ref #100

  • improved C++ API, ref #89

  • Python bindings support with Anaconda, ref #111

  • configure script now tries to detect numpy when building Python bindings, ref #113

  • Python bindings now have embedded documentation, ref #114

  • support for Travis continuous integration, ref #122

  • lower resolution random seed initialization


Logo Machine Learning Support System MALSS 0.5.0

by canard0328 - February 20, 2015, 15:56:02 CET [ Project Homepage BibTeX Download ] 835 views, 215 downloads, 1 subscription

About: MALSS is a python module to facilitate machine learning tasks.

Changes:

Initial Announcement on mloss.org.


Logo Somoclu 1.4.1

by peterwittek - January 28, 2015, 13:19:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7844 views, 1541 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, R, and MATLAB are supported.

Changes:
  • Better support for ICC.
  • Faster code when compiling with GCC.
  • Building instructions and documentation improved.
  • Bug fixes: portability for R, using native R random number generator.

Logo fertilized forests 1.0beta

by Chrisl_S - January 23, 2015, 16:04:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1019 views, 248 downloads, 1 subscription

About: The fertilized forests project has the aim to provide an easy to use, easy to extend, yet fast library for decision forests. It summarizes the research in this field and provides a solid platform to extend it. Offering consistent interfaces to C++, Python and Matlab and being available for all major compilers gives the user high flexibility for using the library.

Changes:

Initial Announcement on mloss.org.


Logo Rabit 0.1.0

by crowwork - January 21, 2015, 18:48:46 CET [ Project Homepage BibTeX Download ] 725 views, 237 downloads, 1 subscription

About: rabit (Reliable Allreduce and Broadcast Interface) is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast for portable , scalable and reliable distributed machine learning programs. Rabit programs can run on various platforms such as Hadoop, MPI and no installation is needed. Rabit now support kmeans clustering, and distributed xgboost: an extremely efficient disrtibuted boosted tree(GBDT) toolkit.

Changes:

Initial Announcement on mloss.org.


Logo ExtRESCAL 0.7.2

by nzhiltsov - January 20, 2015, 00:35:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5090 views, 978 downloads, 2 subscriptions

About: Scalable tensor factorization

Changes:
  • Improve (speed up) initialization of A by summation

Logo pyGPs 1.3.2

by mn - January 17, 2015, 13:08:43 CET [ Project Homepage BibTeX Download ] 4948 views, 1183 downloads, 4 subscriptions

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning.

Changes:

Changelog pyGPs v1.3.2

December 15th 2014

  • pyGPs added to pip
  • mathematical definitions of kernel functions available in documentation
  • more error message added

Showing Items 1-20 of 104 on page 1 of 6: 1 2 3 4 5 6 Next