data structures

pandas

{pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language}

pandas example pandas repository

deep learning

theano

{Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

1. tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
2. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.
3. efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
4. speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
5. dynamic C code generation – Evaluate expressions faster.
6. extensive unit-testing and self-verification – Detect and diagnose many types of errors. }

numpy example numpy repository

tensorflow

{TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.}

tensorflow example tensorflow repository

keras

{Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

1. Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
2. Supports both convolutional networks and recurrent networks, as well as combinations of the two.
3. Runs seamlessly on CPU and GPU. }

keras example keras repository

machine learning

{Machine learning (ML) is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task}

mlpy

mlpy example mlpy repository

sci-kit-learn

{Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.[3] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy}

scikit-learn scikit-learn

scientific computing

numpy

{NumPy is the fundamental package for scientific computing with Python. It contains among other things:

a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code
useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.}

numpy examples numpy repository

scipy

{SciPy (pronounced /ˈsaɪpaɪ’/ “Sigh Pie”[3]) is a free and open-source Python library used for scientific computing and technical computing.

SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. }

scipy example scipy repository

matpilotlib

{matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[3] SciPy makes use of matplotlib.} matpilotlib example matpilotlib repository

plotly

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plotly example plotly repository

web scraping

beautifulsoup

{Beautiful Soup is a Python package for parsing HTML and XML documents (including having malformed markup, i.e. non-closed tags, so named after tag soup). It creates a parse tree for parsed pages that can be used to extract data from HTML, which is useful for web scraping.[1]

It is available for Python 2.6+ and Python 3. }

beautifulsoup example beautifulsoup repository

scrapy

{Scrapy is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler.[3] It is currently maintained by Scrapinghub Ltd., a web-scraping development and services company. }

scrapy example scrapy repository