# 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. http://greenteapress.com/wp/think-dsp/

## 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.

## CISC-106 CS for Engineers¶

Notebooks for CISC 106 - general compute science for engineers by DavidGinzberg

## Computational and Inferential Thinking: The Foundations of Data Science¶

The textbook Computational and Inferential Thinking: The Foundations of Data Science http://www.inferentialthinking.com

## Curso de Herramientas Computacionales - Primer Semestre 2019¶

No description available

## 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

## Efficient Automated Data Analysis, using snakemake¶

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.

## Lab2Learn: Lab environment for Jupyter applications.¶

No description available

## 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*

## 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¶

No description available