Jupyter-notebook Libraries

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Jupyter-notebook Libraries

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…

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.

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…

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 …

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…

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…

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…

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

For example: Recommended versions:

Multiplatform plotting library based on the Grammar of Graphics

This grammar [...] is made up of a set of independent components that can be composed in many different ways. This makes [it] very powerful because you are not limited to a set of pre-specified graph…

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.…

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. T…

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…

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

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!

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 …

This repository contains mini projects in machine learning with notebook files

This repository contains mini projects in machine learning with jupyter notebook files. Go to the projects folder and see the readme for detailed instructions about the projects.

H2O.ai Machine Learning Interpretability Resources

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…

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