Python is a widely used high-level, general-purpose, interpreted, dynamic programming language.[24][25] Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in languages such as C++ or Java.[26][27] The language provides constructs intended to enable writing clear programs on both a small and large scale.[28]

Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.[29]

Python interpreters are available for many operating systems, allowing Python code to run on a wide variety of systems. Using third-party tools, such as Py2exe or Pyinstaller,[30] Python code can be packaged into stand-alone executable programs for some of the most popular operating systems, so Python-based software can be distributed to, and used on, those environments with no need to install a Python interpreter.

CPython, the reference implementation of Python, is free and open-source software and has a community-based development model, as do nearly all of its variant implementations. CPython is managed by the non-profit Python Software Foundation.

Posts

Graph nets

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Learning from graphs rather than from tabular data.

Cross validated predictions

Crossval predictions instead of the usual fit-predict approach.

Kernel visualization

Kernels can be seen as histogram generalizations.

Visualizing decision tree boundaries

Visualizing the decision intersections for a 2D classification via decision trees.

Isotonic regression

A lesser-known, step-like function approximation method.

Forests for feature importance

A classic example of using a (random) forest classifier to sort features.

Grid searching the optimal hyperparameter

Using sklearn GridSearch to optimize hyperparameters.

Speech recognition

Using MFCC for speech recognition.

Outlier detection

Detecting outliers in 2D via various ways.

Topic modeling

Topic modeling with LDA.