Besides the default software environment, CoCalc also offers customizable software environments. They bundle content for a specific purpose with software and libraries in order to form a runnable environment for them.
This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation.
Computational Mechanics & Discrete Information Theory
This repository includes easy-to-run example notebooks for Dask. They are intended to be educational and give users a start on common workflows.
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This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in the second edition of my O’Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow:
This repository contains all lectures from the course Scientific programming in Python that is part of the Cognitive Science program at the University Osnabrück. Each lecture is accompanied by a Jupyter notebook that explains each topic with a combination of code and text.
This is the Jupyter notebook version of the following book: Michael Beyeler: “Machine Learning for OpenCV: Intelligent Image Processing with Python”, 14 July 2017, Packt Publishing Ltd., London, England
Modeling and Simulation in Python is an introduction to physical modeling using a computational approach. It is organized in three parts:
- The first part presents discrete models, including a bikeshare system and world population growth.
- The second part introduces first-order systems, including models of infectious disease, thermal systems, and pharmacokinetics.
- The third part is about second-order systems, including mechanical systems like projectiles, celestial mechanics, and rotating rigid bodies.
An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks. Work in progress.