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[1] it has a simple, flexible and easily readable syntax It was developed by the google. [2] its popularity results in a vast ecosystem of libraries, including for deep learning, such as pytorch, tensorflow, keras, google jax

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The library numpy can be used for manipulating arrays, scipy for scientific and mathematical. [3][4] it is one of the most popular deep learning frameworks, alongside others such as pytorch These include software libraries, frameworks, platforms, and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence, and more.

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Jupyter notebook — an ide that supports markdown, python, julia — r and several other languages

Kaggle notebooks — an online ide for python and r with integrated data science libraries, free gpus, and collaborative features Komodo ide — an ide photos python, perl, php and ruby. It aimed to standardize how environments are defined in ai research, making published research more easily reproducible [3][4] while providing users with a simple interface for interacting with these. Pages in category python (programming language) libraries the following 45 pages are in this category, out of 45 total

This list may not reflect recent changes. List of data science software this is a list of data science software and platforms used in data science, which includes programming languages, programming environments, machine learning frameworks, data engineering tools, statistical software, data analysis, plotting, mlops systems, and more. The natural language toolkit, or more commonly nltk, is a suite of libraries and programs for symbolic and statistical natural language processing (nlp) for english written in the python programming language It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities

Tensorflow is a software library for machine learning and artificial intelligence

It can be used across a range of tasks, but is used mainly for training and inference of neural networks