Ready-to-run Docker images containing Jupyter applications
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Updated
Dec 3, 2024 - Python
The Jupyter Notebook, previously known as the IPython Notebook, is a language-agnostic HTML notebook application for Project Jupyter. Jupyter notebooks are documents that allow for creating and sharing live code, equations, visualizations, and narrative text together. People use them for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.
Ready-to-run Docker images containing Jupyter applications
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
Kandinsky 2 — multilingual text2image latent diffusion model
Jupyter notebooks in the terminal
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
strip output from Jupyter and IPython notebooks
Instructional notebooks on music information retrieval.
CatBoost tutorials repository
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.
Non-Intrusive Load Monitoring Toolkit (nilmtk)
This repository contains all the data analytics projects that I've worked on in python.
Scipy Cookbook
A py.test plugin to validate Jupyter notebooks
A tiny 1000 line LLVM-based numeric specializer for scientific Python code.
IPython Notebooks to learn Python
Jupyter for Visual Studio Code
Pytest in IPython notebooks.
Jupyter Notebooks from the old UnsupervisedLearning.com (RIP) machine learning and statistics blog
Real-time GCC-NMF Blind Speech Separation and Enhancement
Created by Fernando Pérez, Brian Granger, and Min Ragan-Kelley
Released December 2011
Latest release Today