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.



It is a common misconception that AI is absolutely objective, since AI is objective only in the sense of learning what human teaches.

Text classification with Tensorflow 2.0

The classic IMDB classification based on Tensoflow 2.0

Fashion MNIST using Temsorflow 2.0

The MNIST fashion set consists of images of clothing, like sneakers and shirts. It's somewhat more complex than the the classic MNIST dataset.

Serving TensorFlow models

Restify TF networks and other ways to serve intelligence.

The Keras Functional API

The Keras API makes creating deep learning models fast and easy.

Graph nets

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.