Introduction to libraries
If you need an intro/introduction to any of the following libraries NumPy, Pandas, TensorFlow, Keras, jQuery UI, please watch my videos on my YouTube playlist “Introduction to libraries”.
Introduction to NumPy
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers.
In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications.
NumPy is open-source software and has many contributors.
NumPy is a NumFOCUS fiscally sponsored project.
Content source:
https://en.wikipedia.org/wiki/NumPy
Introduction to Pandas
Pandas is a software library written for the Python programming language for data manipulation and analysis.
In particular, it offers data structures and operations for manipulating numerical tables and time series.
It is free software released under the three-clause BSD license.
The name is derived from the term “panel data”, an econometrics term for data sets that include observations over multiple time periods for the same individuals.
Its name is a play on the phrase “Python data analysis” itself.
Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010.
Content source:
https://en.wikipedia.org/wiki/Pandas_(software)
Introduction to TensorFlow
TensorFlow is a free and open-source software library for machine learning and artificial intelligence.
It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
TensorFlow was developed by the Google Brain team for internal Google use in research and production.
The initial version was released under the Apache License 2.0 in 2015.
Google released the updated version of TensorFlow, named TensorFlow 2.0, in September 2019.
TensorFlow can be used in a wide variety of programming languages, most notably Python, as well as Javascript, C++, and Java.
This flexibility lends itself to a range of applications in many different sectors.
Content source:
https://en.wikipedia.org/wiki/TensorFlow
Introduction to Keras
Keras is an open-source software library that provides a Python interface for artificial neural networks.
Keras acts as an interface for the TensorFlow library.
Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML.
As of version 2.4, only TensorFlow is supported.
Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.
It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer.
Chollet is also the author of the Xception deep neural network model.
Content source: