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easy-tensorflow - Simple and comprehensive tutorials in TensorFlow

  •    Python

The goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format).

colibri-core - Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i

  •    C++

Skipgram and flexgram extraction are computationally more demanding but have been implemented with similar optimisations. Skipgrams are computed by abstracting over n-grams, and flexgrams in turn are computed either by abstracting over skipgrams, or directly from n-grams on the basis of co-occurrence information (mutual pointwise information). At the heart of the sofware is the notion of pattern models. The core tool, to be used from the command-line, is colibri-patternmodeller which enables you to build pattern models, generate statistical reports, query for specific patterns and relations, and manipulate models.

ECoL - Extended Complexity Library in R

  •    R

The Extended Complexity Library (ECoL) is the implementation in R of a set of measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. The measures were originally proposed by Ho and Basu [1] and extend by many other works including the DCoL library [2]. The main difference between the libraries is that ECoL provides bug fixes, generalizations and implementations of many other state-of-the-art measures. The measures can be divided into two groups: classification and regression measures. The classification measures are based on: (1) feature overlapping measures, (2) neighborhood measures, (3) linearity measures, (4) dimensionality measures, (5) class balance measures and (6) network measures. The regression measures are based on: (3) linearity measures, (7) correlation measures, (4) dimensionality measures and (8) smoothness measures.









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