Notebook Libraries

116 Components & Libraries

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Notebook Libraries

Interactive Data Visualization in the browser, from Python

Non-monetary support can help with development, collaboration, infrastructure, security, and vulnerability management. The Bokeh project is grateful to the following companies for their donation of s…

Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

After some recent success of Bayesian methods in machine-learning competitions, I decided to investigate the subject again. Even with my mathematical background, it took me three straight-days of rea…

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

This is a tutorial in an IPython Notebook for the Kaggle competition, Titanic Machine Learning From Disaster. The goal of this repository is to provide an example of a competitive analysis for those …

Some Jupyter notebooks about audio signal processing with Python

You may consume them in different ways:

NoteBooks-Statistics-and-MachineLearning

Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog

Notebooks as published on unsupervised-learning.com (RIP).

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

Collection of geophysical notes in the form of IPython/Jupyter notebooks.

My collection of geophysical notes written as Jupyter notebooks. (Mostly obsolete now, don't have time for a proper update; for example I stopped wasting my time with jupyter widgets and Panel for in…

CoCalc: Collaborative Calculation in the Cloud

The copyright of CoCalc is owned by SageMath, Inc., and the source code here is released under the GNU Affero General Public License version 3+ subject to the "Commons Clause" License Condition v1.0.…

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

The recommended way to fully experience the book is by running the Jupyter notebooks: You can test your installation by running

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

The reference book for these and other Spark related topics is: The following notebooks can be examined individually, although there is a more or less linear 'story' when followed in sequence. By usi…

Python Quick Reference

Here's what I want in a reference guide: Click to jump to the relevant section of the script or the notebook: Thank you!

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

Graphistry gets used on problems like visually mapping the behavior of devices and users, investigating fraud, analyzing machine learning results, and starting in graph AI. It provides point-and-clic…

Ways of doing Data Science Engineering and Machine Learning in R and Python

These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do…

Ready-to-run Docker images containing Jupyter applications

The container remains intact for restart after the Server exits.

:earth_americas: machine learning tutorials (mainly in Python3)

This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics. Curated notes on deep learning. Notes related to advertising domai…

Data journalism and easy to replicate notebooks using Python, R, and Web visualisations

So our hope is that while finding our articles analysis and conclusions interesting, you will also learn how to repeat and extend them yourself and arrive to aditional conclusions. Where we analyse t…

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Use any of the following services. Next, run the following commands: Finally, start Jupyter: I recommend Python 3.7. If you follow the installation instructions above, that's the version you will get…

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

pytablewriter is a Python library to write a table in various formats: AsciiDoc / CSV / Elasticsearch / HTML / JavaScript / JSON / LaTeX / LDJSON / LTSV / Markdown / MediaWiki / NumPy / Excel / Pandas / Python / reStructuredText / SQLite / TOML / TSV.

Some of the formats require additional dependency packages, you can install these packages as follows: You can apply styles to specific cells by using style filters. Style filters will be written as …

:notebook: A growing collection of Jupyter Notebooks written in Python, OCaml and Julia for science examples, algorithms, visualizations etc

You can also launch an interactive environment to play with the code by yourself, using one of these platforms: And more will come... soon! 🚅 You can also launch an interactive environment to play wi…

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.

Analyzing the IRIS dataset with Sci-Pype This notebook shows how to make new predictions with cached IRIS Classifier Models + Analysis housed in redis. This notebook shows how to make new predictions…

Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).

We believe mastery of a certain machine learning concept/topic is achieved only when the answer to each of the following three questions is affirmative. Example ”roadmaps” shown below provide suggest…

Python code for "Probabilistic Machine learning" book by Kevin Murphy

The notebooks needed to make all the figures are available at the following locations. You can use any of the following options to install the other requirements. Run Then install manually.

New way to do academic writing.

WillNotebook is inspired by jupyter (notebook) project, but aims the specifics of academic writing. The idea is to merge the advantages and functionalities of Word, Latex and Python together into one…

대학 강의를 위한 강의자료 및 소스코드 저장소 Python Lecture slide & code for University Student.

2016년 겨울방학 중앙대학교 Python 기초과목 구글 파이썬 스타일 가이드로 시작하는 프로그래밍 Ver 1.1.1 (Last Update 2018-06-14) 본 Repository는 GPL-3.0 라이센스를 따릅니다. 상업적, 교육용, 개인적인 사용이 가능합니다. 자세한 라이센스 제약사항은 본 글 하단을 참조하세요. 강의 슬라이드는 복습 =&g…

PRML algorithms implemented in Python

Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning" From the table below, you can open the notebooks for each chapter in each of these environme…

M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

This is the Jupyter notebook version of the following book: If you use either book or code in a scholarly publication, please cite as: M. Beyeler, (2017). Machine Learning for OpenCV. Packt Publishin…

babble-rnn is a research project in the use of machine learning to generate new speech by modelling human speech audio, without any intermediate text or word representations. The idea is to learn to speak through imitation, much like a baby might.

This babbler is a stack of 11 bidirectional LSTMs, attempting to learning an encoded sequence of data (frame of 13 normalized parameters, representing 20ms of audio).Groups of LSTMs are trained toget…

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