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GitHub - explosion/spaCy: 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
nlp  python 
13 hours ago by twleung
A Closer Look at the ‘Learning’ Aspect of Machine Learning
"Each algorithm is designed to either predict or classify data. For example, logistic regression is used to find out a boolean value that results in a true or false. linear regression, as we have seen, is used in scenarios where the prediction is a number. Other algorithms like Support Vector Machine and K Nearest Neighbor are used for classification. Irrespective of the algorithm, the goal is to use existing data to find accurate parameters to evolve the final model."
technology  top  stories  tutorials  artificial  intelligence  coefficient  of  determination  cost  functions  development  linear  regression  machine  learning  is  not  magic  python  supervised 
14 hours ago by jonerp
Reproducible research in the Python ecosystem: a reality check
A few years ago, I decided to adopt the practices of reproducible research as far as possible within the technical and social constraints I have to live with. So how reproducible is my published code over time?

The example I have chosen for this reproducibility study is a 2013 paper about computing diffusion coefficients from molecular simulations. All code and data has been published as an ActivePaper on figshare. To save space, intermediate results had been removed from the published archive. This makes my reproducibility check very straightforward: a simple aptool update will recompute everything starting from these intermediate results up to the plots that went into the paper.

One nice aspect of ActivePapers is that it stores the version numbers of all dependencies, so I can quickly verify that in 2013, I had used Python 2.7.3, NumPy 1.6.2, h5py 2.1.3, and matplotlib 1.2.x (yes, the x is part of the reported version number).
Python  research 
14 hours ago by sechilds

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