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Implementation
Published in Seyedeh Leili Mirtaheri, Reza Shahbazian, Machine Learning Theory to Applications, 2022
Seyedeh Leili Mirtaheri, Reza Shahbazian
Scikit-Learn is generally known as a leading open-source tool for Python in which there is an inclusive library for Machine Learning algorithms. David Cournapeau started it as a Google Summer of Code project. Following 2015, Scikit-Learn is under active development sponsored by Telecom ParisTech, INRIA, and sometimes Google through its Summer of Code. Scikit-Learn has broadened the functionality of SciPy and NumPy packages along with many Machine Learning algorithms while offering functions to perform regression, classification, clustering, model selection, preprocessing, and dimensionality reduction. Matplotlib package is also used by Scikit-Learn to plot charts. From April 2016, it is conveyed in together-developed Anaconda for Cloudera project on Hadoop clusters. Along with Scikit-Learn, Anaconda consists of many popular packages for science, mathematics, and engineering for the Python ecosystem, namely Pandas, SciPy and NumPy.
Introduction to Python
Published in Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, L. Srinivasa Varadharajan, Understanding Optics with Python, 2018
Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, L. Srinivasa Varadharajan
Anaconda (https://www.continuum.io/downloads) is an easy-to-install free package and environment manager that contains Python distribution (Python 2.x or Python 3.x), and a collection of over 150 open source pre-built and tested scientific and analytic Python packages such as NumPy, Pandas, SciPy, Matplotlib, and IPython, with over 250 more packages that can be installed through Anaconda. In addition, Anaconda includes several open source integrated development environments (IDE) such as Jupyter/IPython and Spyder. Anaconda is available for Linux, OS X, and Windows. Download the Python 3.x distribution for your operating system, check for the integrity of the downloaded file by verifying its cryptographic hashes (MD5 or SHA-256) given in the same page, and then follow the instructions to install the package. During the installation process, if you are unsure of any setting, simply accept the default since these can be changed later.
Introduction
Published in Vladimir A. Dobrushkin, Applied Differential Equations with Boundary Value Problems, 2017
Python7 is a high-level and general-purpose programming language (free of charge). Part of the reason that it is a popular choice for scientists and engineers is the language versatility, online community of users, and powerful analysis packages such as NumPy, SciPy, and, of course, SymPy, a CAS written completely in Python. Anaconda is a free Python distribution from Continuum Analytics that includes many useful packages for scientific computing. The function odeint is available in SciPy for integrating first order vector differential equations. A higher order ordinary differential equation can always be reduced to a differential equation of this type by introducing intermediate derivatives into the vector (see §6.3). There are many optional inputs and outputs available when using odeint that can help tune the solver.
Forest Change Detection Using an Optimized Convolution Neural Network
Published in IETE Technical Review, 2022
Radha Senthilkumar, V. Srinidhi, S. Neelavathi, S. Renuga Devi
Anaconda is a free and open source platform for compiling and executing Python and R programs. It is extensively used in scientific computing and data analytics tasks such as machine learning, predictive analytics, large-scale data processing, image processing, sentimental analysis, etc. The package versions in anaconda are managed by the package management system conda.
Tutorial: The LuxPy Python Toolbox for Lighting and Color Science
Published in LEUKOS, 2020
Spyder is an Interactive Development Environment (IDE) for scientific programming in the Python language. It supports IPython and popular Python libraries such as NumPy, SciPy, or matplotlib. It should come installed with the Anaconda distribution in the conda virtual environment. If not, it can be installed by typing: