Notebook Libraries

115 Components & Libraries

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Notebook Libraries

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

It just takes a few lines of Python to create a beautiful demo like the one above, so let's get started 💫 You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere …

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 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…

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

In regression problems we do real value predictions. Basically we try to draw a line/plane/n-dimensional plane along the training examples. In classification problems we split input examples by certa…

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…

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…

This repository contains all the DSA (Data-Structures, Algorithms, 450 DSA by Love Babbar Bhaiya, FAANG Questions), Technical Subjects (OS + DBMS + SQL + CN + OOPs) Theory+Questions, FAANG Interview questions, and Miscellaneous Stuff (Programming MCQs, Puzzles, Aptitude, Reasoning). The Programming languages used for demonstration are C++, Python, and Java.

We'd love your contributions! Kindly follow the steps below to get started: Clone the forked repository. Make changes. Stage your changes and commit

Ready-to-run Docker images containing Jupyter applications

The container remains intact for restart after the Server exits.

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

Have you always wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diffs when doing version control? Then, Jupytext may …

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.

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

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

Welcome to the Azure Machine Learning Python SDK notebooks repository! Install additional packages as needed: This repository is a push-only mirror. Pull requests are ignored.

pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON…

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

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.

IPython Notebook(s) and other programming tools such as Torch/Lua/D lang in demonstrating deep learning functionality. Additional PyRo tutorials: IPython Notebook(s) demonstrating scikit-learn functi…

Debugging, monitoring and visualization for Python Machine Learning and Data Science

TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualiza…

The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

A simple YAML API to get started quickly, a powerful Python API for total flexibility. Automatically cache your pipeline’s previous results and only re-compute tasks that have changed since your last…

🛠 All-in-one web-based IDE specialized for machine learning and data science.

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on you…

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling you to thoroughly test your data and models from research to production. Deepchecks includes: For …

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

There are eight subjects in the curriculum, organized into four subject areas. See the "Machine Learning House" section below for detail on why these are the essential foundational subject areas: Lat…

: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…

A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.

(aka the "awesome-notebooks") The objective of this repository is to create the largest catalog of production-ready Jupyter Notebooks templates. With those templates, it becomes easy to create data p…

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

This repository contains all the code, notebooks and examples used in this book. We will also be adding bonus content here from time to time. So keep watching this space! Master the essential skills …

Turn Python scripts into handouts with Markdown and figures

Turn Python scripts into handouts with Markdown comments and inline figures. An alternative to Jupyter notebooks without hidden state that supports any text editor. You use Python Handout as a librar…

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…

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…

Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.

Because NannyML can estimate performance, it is possible to weed out data drift alerts that do not impact expected performance, combatting alert fatigue. Besides linking data drift issues to drops in…

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…