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probml/pml-book

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Pml Book

This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks.

The project emphasizes reproducibility through a collection of interactive notebooks and standalone scripts used to recreate data plots and figures from the text. These materials are hosted in external environments to allow users to execute complex machine learning code without local installation.

The educational surface includes lecture slides, exercise solutions, and supplementary documents that provide additional technical details. Content is organized using a markdown-driven structure and managed via version control to maintain consistency across book editions.

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Features

  • Probabilistic Machine Learning Study Guides - Provides structured text and supplementary guides for mastering topics from basic probability to neural networks.
  • Markdown Content Structures - Organizes educational text into hierarchical markdown files processed by a static site generator.
  • Notebook-Based Experimentation - Provides interactive notebooks that combine code and documentation to generate the figures and computations described in the text.
  • Educational Code Repositories - Provides source code collections specifically curated to accompany the educational textbook.
  • Educational Content - Offers a comprehensive collection of text and supplemental materials for study and reference.
  • Educational Examples - Includes illustrative code samples and interactive notebooks designed for instructional purposes.
  • Probabilistic Programming - Provides educational resources and code demonstrations for implementing probabilistic programming concepts.
  • AI & Machine Learning Education - Provides educational content covering neural network theory and the mathematical foundations of probability.
  • Notebook-Based Figure Generation - Executes interactive Python cells to compute data and render visual plots used throughout the text.
  • Figure Recreation - Allows users to execute code to exactly reproduce visual results and plots presented in the technical text.
  • Notebook Environment Integrations - Integrates with cloud notebook platforms to allow readers to execute code without local dependency installation.
  • Study Materials - Provides exercise solutions and lecture slides to support the theoretical study of probabilistic machine learning.
  • Machine Learning Lectures - Provides academic lectures and theoretical explanations to support the study of probabilistic models.
  • Supplementary Technical Documents - Provides downloadable documents containing additional technical details and extended content for the core series.
  • Reproducible Research Documents - Ships interactive notebooks and scripts that pair narrative text with executable code to recreate textbook figures.
  • Educational Computational Scripts - Provides specialized scripts to regenerate figures and perform computations described in the text.
  • Machine Learning - Comprehensive probabilistic machine learning textbook covering core concepts.
5,598 stars·651 forks·Jupyter Notebook·MIT·25 views

Star history

Star history chart for probml/pml-bookStar history chart for probml/pml-book

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does probml/pml-book do?

This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks.

What are the main features of probml/pml-book?

The main features of probml/pml-book are: Probabilistic Machine Learning Study Guides, Markdown Content Structures, Notebook-Based Experimentation, Educational Code Repositories, Educational Content, Educational Examples, Probabilistic Programming, AI & Machine Learning Education.

Which projects share features with probml/pml-book?

Projects with overlapping indexed features include: hacker0x01/hacker101 — Hacker101 is a cybersecurity education platform and web security training portal. It serves as a structured collection… mleveryday/practicalai-cn — This project is an educational course and machine learning curriculum designed to teach the implementation of neural… llsourcell/learn_machine_learning_in_3_months — This project is a machine learning curriculum and educational course repository designed as a structured three-month… pkmital/tensorflow_tutorials — This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and… eriklindernoren/ml-from-scratch — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built… gskinnerteam/flutter-wonderous-app — This project is a cross-platform educational application built with the Flutter framework. It serves as a mobile…

Projects sharing features with Pml Book

These projects share indexed features with Pml Book. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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  • hacker0x01/hacker101Hacker0x01 avatar

    Hacker0x01/hacker101

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    Hacker101 is a cybersecurity education platform and web security training portal. It serves as a structured collection of lessons and resources designed to teach students about vulnerability research and penetration testing through guided modules. The platform operates as a static site generator and markdown-based content manager. It uses plain text files with structured metadata to define the hierarchy and properties of educational lessons, transforming this content into pre-rendered HTML files for delivery. The curriculum covers a broad domain of security education, including specialized c

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  • pkmital/tensorflow_tutorialspkmital avatar

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    This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and tensor operations using the TensorFlow framework. It serves as a machine learning educational repository and implementation guide for deep learning students. The suite focuses on specific advanced architectures, including convolutional networks for image classification, residual networks with skip connections for training stability, and variational autoencoders for generative modeling and data synthesis. It also includes guides for building denoising and deep autoencoders to pe

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