Custom Software

Besides the default software environment, CoCalc also offers customizable software envioronments. They bundle content for a specific purpose with software and libraries in order to form a runnable environment for them.

100 numpy exercises

This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation.

Allen Downey Think Dsp

Think DSP: Digital Signal Processing in Python, by Allen B. Downey.

Brittle Matrix Composite Structures (RWTH Aachen)

The course is divided into three blocks related to detailed theoretical and experimental description of (1) bond, debonding process, anchorage, (2) crack initiation and propagation, and (3) multiple cracking, matrix fragmentation process under elementary loading conditions. Each of these aspects is first treated in an abstract way using theoretical and numerical methods. The general part is followed with examples of particular types of material components and practical tasks related to the either material development, design & dimensioning or safety assessment of structures.


No description available

Computational and Inferential Thinking: The Foundations of Data Science

The textbook Computational and Inferential Thinking: The Foundations of Data Science

Dask Example Notebooks

This repository includes easy-to-run example notebooks for Dask. They are intended to be educational and give users a start on common workflows.

Deep Learning Workshop

No description available

Geopandas Tutorial

No description available

Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow 2.0

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:

Ipython In Depth

IPython in-depth Tutorial, first presented at PyCon 2012

Jupyterlab Demo

This repository contains some demonstrations of JupyterLab, the next generation user interface of Project Jupyter.

Kalman and Bayesian Filters in Python

Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser.

Lectures in Scientific Computing in Python

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.

LFortran: Fortran in Jupyter

No description available

LIGO Binder (by minrk)

A binder for doing a live demo of the LIGO tutorial.

Small fixes and updates from the original.

Machine Learning for OpenCV

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

ModSimPython: Modeling and Simulation in Python

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.

Numba Examples

Example Numba implementations of functions

Open Security Summit 2019

Open Security Summit 2019, England, 3-7 June 2019 The Open Security Summit is focused on the collaboration between Developers and Application Security.

Python Data Science Handbook

No description available

Python Outlier Detection (PyOD)

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products. It is also well acknowledged by the machine learning community with various dedicated posts/tutorials

Qiskit Tutorials

A collection of Jupyter notebooks from the community and qiskit developers showing how to use Qiskit.

QuantStack xeus-cling

xeus-cling is a Jupyter kernel for C++ based on the C++ interpreter cling and the native implementation of the Jupyter protocol xeus.

QuantStack xeus-python

xeus-python is a Jupyter kernel for Python based on the native implementation of the Jupyter protocol xeus.

QuantStack xtensor

Multi-dimensional arrays with broadcasting and lazy computing.

Riemann Problems and Jupyter Solutions

An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks. Work in progress.

Scientific Python Stack

A basic selection of Python 3.7 libraries (pandas, scikit, sympy, …), Octave, and LaTeX setup.

Tensorflow 2

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