#Jupyter Notebooks

Discover the basics of Machine Learning with Python using practical examples and interactive notebooks. This project accompanies the first edition of 'Hands-On Machine Learning with Scikit-Learn and TensorFlow,' including exercises and code solutions. The notebooks can be accessed online through Colaboratory, Binder, or Deepnote, and executed locally with an Anaconda setup recommended for Python 3.7. It offers a thorough educational experience for those interested in applying machine learning principles through practical scenarios.

Enhance Sphinx documentation with a Jupyter-compatible theme using Bootstrap 5 for vibrant visuals and adaptive layouts. Enjoy interactive notebook features and real-time engagement via BinderHub integration. Quick installation and configuration make transformation effortless.

Explore a collection of sample applications and Jupyter Notebooks to understand Pinecone's vector databases and AI techniques. This repo includes examples for both practical use and educational purposes, maintained by Pinecone experts. Ideal for developers aiming to experiment and create diverse AI applications with detailed guides and documentation. Contributions are welcome to enhance this community resource.

Explore this Visual Studio Code extension designed to enhance Python coding with efficient linting and formatting capabilities. This extension combines tools such as Flake8, Black, and isort under one interface, improving code quality and streamlining development. Built with Rust, it supports Jupyter Notebooks and untrusted workspaces, offering robust compatibility across various environments. Features include managing imports, applying 'Quick Fix' actions, and maintaining consistent formatting, all contributing to a fluid and productive coding experience.

This open-source book provides a rich learning resource in deep learning, featuring Jupyter notebooks, executable code, and thorough discussions. It serves as a free educational tool designed to help future machine learning professionals develop technical expertise and practical problem-solving capabilities, with regular updates and community engagement enhancing the overall learning experience.

Discover a rich collection of Jupyter Notebooks and code in Python, HTML5, and D3.JS, tailored for data scientists looking to explore and learn about data collection, preprocessing, analysis, visualization, and narrative techniques. Including insights on sentiment analysis, scikit-learn and PyCaret workflows, and innovative visualization methods with Altair and Plotly, this project offers comprehensive resources available across multiple platforms, backed by detailed documentation and curated tutorials on Medium.

Explore a variety of interactive machine learning experiments utilizing Jupyter/Colab notebooks and demo pages. This project encompasses a range of concepts, from supervised learning with TensorFlow and Keras to CNNs and RNNs. Serving as an educational platform, it allows for the practice of different algorithms and datasets. Suitable for experimentation with models such as Multilayer Perceptron for digit recognition and CNNs for image classification. Gain insights into model training and performance through comprehensive demos and sessions. This project is intended for educational exploration rather than optimized deployment.