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parallel-gbdt

Parallel Gradient Boosting Decision Trees

parallel-large-scale-feature-selection

implementation of the method in this paper "Parallel Large Scale Feature Selection for Logistic Regression "

parallel_ml_tutorial

Tutorial on scikit-learn and IPython for parallel machine learning

parameterimportance

Parameter Analysis Tool

parameters

Ensuring sustainability in reverse supply chain in case of ripple effect: A two-stage stochastic optimization model

pareto.py

Nondominated sorting for multi-objective problems

parprog-snippets

Snippets and programs from the Parallel Programming lectures.

parsenbastatintocsv

parser-model

A neural network with a sparse input, for predicting decisions of a natural language syntax parser.

parsing-raw-files-text-pre-processing

Text documents, such as crawled web data, are usually comprised of topically coherent text data, which within each topically coherent data, one would expect that the word usage demonstrates more consistent lexical distributions than that across data-set. A linear partition of texts into topic segments can be used for text analysis tasks, such as passage retrieval in IR (information retrieval), document summarization, recommender systems, and learning-to-rank methods.

part2

partials_plots_nba_example

Using NBA shooting data to demonstrate partial plots from a multi-layer neural network

particlebasedcommunitydetection

passage

A little library for text analysis with RNNs.

pastas

:spaghetti: Pastas is an open-source Python framework for the analysis of hydrological time series.

patch-learning

pathclassifiernn

Convolution Neural Network to classify strings as malware

pathnre

Source code and dataset of EMNLP2017 paper "Incorporating Relation Paths in Neural Relation Extraction".

pathpy

An OpenSource python package for the analysis of sequential data on pathways and temporal networks using multi-order graphical models

pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

pavlov.js

Reinforcement learning using Markov Decision Processes

paysim

Financial Simulator of Mobile Money Service

pbbslib

pbda

PAC-Bayesian Domain Adaptation (aka PBDA) -- machine learning algorithm

pbl_15_twitterfeedsentimentanalysis

pbl_15_twittersentimentanalysis

pbmf

One of the most important problem of image processing is the task of pre-cleaning them from noise. There are many well-accepted methods for image filtering. However, along with their advantages they have their drawbacks. Thus, the task of combining several filters into one filter seems to be relevant. The problem of constructing an aggregating filter with the use of tools of evidence theory (the Dempster-Shafer theory) is considered in this paper. The efficiency of constructing such an operator with using various rules for combining evidences and considering their discounting is investigated. Experimental testing was conducted for various types of noise. The comparative analysis of the efficiency of image filtering with aggregation filters with the classical filtering methods was carried out with respect to the various cost functionals.

pbpnba

Play by Play data download and analysis tools from nba.com

pbpython

Code, Notebooks and Examples from Practical Business Python

pca-dimension-reduction

Reducing High Dimensional Data with Principle Component Analysis (PCA)