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r_in_insurance_2013

Files from the R in Insurance conference 15 July 2013

react-spreadsheet-component

:clipboard: Spreadsheet Component for ReactJS

redash

Rethinking how data is queried, shared and visualized. A web application that allows to easily query an existing database, share the dataset and visualize it in different ways. Oh and you can also create dashboards.

reflexion

Reflexion: Language Agents with Verbal Reinforcement Learning

relaxed

Create PDF documents using web technologies

seglearn

Python module for machine learning time series:

sempre

Semantic Parser with Execution

sfa

Scalable Time Series Data Analytics

simpleai

simple artificial intelligence utilities

single-parameter-fit

Real numbers, data science and chaos: How to fit any dataset with a single parameter

soft-dtw

Python implementation of soft-DTW.

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stanford_corenlp_pywrapper

startupreadings

Reading list for all things startup-related

statlearning-notebooks

Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).

statsmodels

Statsmodels: statistical modeling and econometrics in Python

stockinference-spark

Stock inference engine using Spring XD, Apache Geode / GemFire and Spark ML Lib.

structuredinference

Structured Inference Networks for Nonlinear State Space Models

tensorflow

Open source software library for numerical computation using data flow graphs.

termite-data-server

Data Server for Topic Models

time-series-library

A Library for Advanced Deep Time Series Models.

topic_modeling_tutorial

Instructions & code for the EuroPython 2014 training session "Topic Modeling for Fun and Profit"

transporter

Sync data between persistence engines, like ETL only not stodgy

tsfresh

Automatic extraction of relevant features from time series:

tuneta

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

twitter_nlp

Twitter NLP Tools

ucr_time_series_classification_deep_learning_baseline

Fully Convlutional Neural Networks for state-of-the-art time series classification

umbel

UMBEL (Upper Mapping and Binding Exchange Layer) has two purposes. Its first purpose is to provide a general vocabulary (the UMBEL "vocabulary") of classes and predicates for describing domain ontologies, with the specific aim of promoting interoperability with external datasets and domains. The second purpose is to provide a coherent framework of broad subjects and topics (the UMBEL "reference concepts"), suitable as binding nodes for mapping relevant Web-accessible content, also with the specific aim of promoting interoperability and to reason over a coherent reference structure and its linked resources. UMBEL presently has about 28,000 of these reference concepts drawn from the Cyc knowledge base, which are organized into more than 30 mostly disjoint SuperTypes.

universal-portfolios

Collection of algorithms for online portfolio selection

wnext

NLTK wordnet extensions