29 matching results for "python":
Submitted Apr 17, 2017 to Scientific Software Altair is a declarative statistical visualization library for Python, based on VegaLite.
With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful VegaLite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. 
Submitted Apr 15, 2017 (Edited Apr 16, 2017) to Science Courses and Tutorials This notebook was originally prepared for the workshop Advanced Text Analysis with SpaCy and ScikitLearn, presented as part of NYCDH Week 2017. Here, we try out features of the SpaCy library for natural language processing. We also do some statistical analysis using the scikitlearn library.

Submitted Apr 14, 2017 to Scientific Software The nfft package is a lightweight implementation of the nonequispaced fast Fourier transform (NFFT), implemented via numpy and scipy and released under the MIT license. For information about the NFFT algorithm, see the paper Using NFFT 3 – a software library for various nonequispaced fast Fourier transforms.
The nfft package achieves comparable performance to the C package described in that paper, without any customized compiled code. Rather, it makes use of the computational building blocks available in NumPy and SciPy. For a discussion of the algorithm and this implementation, see the Implementation Walkthrough notebook. 
Submitted Mar 28, 2017 (Edited Apr 10, 2017) to Science Courses and Tutorials Although spatial interaction modeling is a fundamental technique to many geographic disciplines, relatively little software exists for spatial interaction modeling and for the analysis of flow data. This applies particularly to the realm of free and open source software. As a result, this primer introduces the recently developed spatial interaction modeling (SpInt) module of the python spatial analysis library (PySAL). The underlying conceptual framework of the module is first highlighted, followed by an overview of the main functionality, which will be illustrated using migration data. Finally, some future additions are discussed.

Submitted Mar 28, 2017 to Science Courses and Tutorials An introduction to quantum computing using the pyQuil Python library. This tutorial covers such topics as qubit operations, Pauli operators, multiqubit operations, the Quantum Abstract Machine, classical/quantum interaction, the probabilistic halting problem, and more, complete with pyQuil code examples.

Submitted Mar 27, 2017 to Scientific Software ScikitFuzzy is a collection of fuzzy logic algorithms intended for use in the SciPy Stack, written in the Python computing language.
This SciKit is developed by the SciPy community. Contributions are welcome! 
Submitted Mar 25, 2017 to Science Courses and Tutorials In this post we will first discuss how to set up Spark to start easily performing analytics, either simply on your local machine or in a cluster on EC2. We then will explore Spark at an introductory level, moving towards an understanding of what Spark is and how it works (hopefully motivating further exploration). In the last two sections we will start to interact with Spark on the command line and then demo how to write a Spark application in Python and submit it to the cluster as a Spark job.

Submitted Mar 17, 2017 (Edited Mar 17, 2017) to Scientific Software xtensor is a C++ library meant for numerical analysis with multidimensional array expressions.
xtensor provides  an extensible expression system enabling lazy broadcasting,  an API following the idioms of the C++ standard library, and  tools to manipulate array expressions and build upon xtensor. Containers of xtensor are inspired by NumPy, the Python array programming library. Adaptors for existing data structures to be plugged into our expression system can easily be written. In fact, xtensor can be used to process numpy data structures inplace using Python’s buffer protocol. For more details on the numpy bindings, check out the xtensorpython project. 
Submitted Mar 11, 2017 to Science Courses and Tutorials This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

Submitted Mar 07, 2017 to Science Blogs As a followup of my previous post on reliability diagrams, I have worked jointly with Alexandre Gramfort, Mathieu Blondel and Balazs Kegl (with reviews by the whole team, in particular Olivier Grisel) on adding probability calibration and reliability diagrams to scikitlearn. Those have been added in the recent 0.16 release of scikitlearn as CalibratedClassifierCV and calibration_curve.
This post contains an interactive version of the documentation in the form of an IPython notebook; parts of the text/code are thus due to my coauthors. 
Submitted Mar 07, 2017 to Science Courses and Tutorials Code reuse is a very common need. It saves you time for writing the same code multiple times, enables leveraging other smart people’s work to make new things happen. Even just for one project, it helps organize code in a modular way so you can maintain each part separately. When it comes to python, it means format your project so it can be easily packaged. This is a simple instruction on how to go from nothing to a package that you can proudly put it in your portfolio to be used by other people.

Submitted Mar 03, 2017 (Edited Mar 03, 2017) to Scientific Software Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming.
Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. 
Submitted Mar 01, 2017 to Scientific Software Scikitplot is an intuitive library to add plotting functionality to scikitlearn objects.
Scikitplot is the result of an unartistic data scientist's dreadful realization that visualization is one of the most crucial components in the data science process, not just a mere afterthought. Gaining insights is simply a lot easier when you're looking at a colored heatmap of a confusion matrix complete with class labels rather than a singleline dump of numbers enclosed in brackets. Besides, if you ever need to present your results to someone (virtually any time anybody hires you to do data science), you show them visualizations, not a bunch of numbers in Excel. That said, there are a number of visualizations that frequently pop up in machine learning. Scikitplot is a humble attempt to provide aestheticallychallenged programmers (such as myself) the opportunity to generate quick and beautiful graphs and plots with as little boilerplate as possible. 
Submitted Feb 26, 2017 to Scientific Software We speed up the image generation algorithm of PixelCNN++ by avoiding redundant computation through caching. Naive generation discards computation that can be reused and performs additional computation that will not be used to generate a particular pixel. Naive generation can take up to 11 minutes to generate 16 32by32 images on a Tesla K40 GPU. By reusing previous computation and only performing the minimum amount of computation required, we achieve up to a 183 times speedup over the naive generation algorithm. We have tested our code with Python 3 and TensorFlow 1.0. You may need to make small changes for other versions of Python or TensorFlow.

Submitted Feb 06, 2017 to Science Books James McCaffrey’s SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this ebook to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming.

Submitted Jan 26, 2017 to Science Courses and Tutorials This Python notebook implements the toy example from Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks by Lars Mescheder, Sebastian Nowozin, Andreas Geiger.


Submitted Jan 26, 2017 to Science Courses and Tutorials An article and tutorial on using linters to manage large, enterprisesized Python projects.

Submitted Jan 21, 2017 to Scientific Software A gallery comparing colorschemes from stylesheets defined in Python Matplotlib with source code available on github..

Submitted Jan 15, 2017 (Edited Jan 15, 2017) to Science Courses and Tutorials Jupyter (formerly known as IPython) notebooks are great – but have you ever accidentally deleted a cell that contained a really important function that you want to keep? Well, this post might help you get it back.
