Projects sharing features with InterpretableMLBook

Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu

nlp-recipes is a collection of implementation guides and reference templates for applying natural language processing techniques to real-world tasks. It provides standardized workflows and code examples for developing NLP pipelines, from dataset preparation and model training to performance evaluation. The project focuses on the practical application of transformer-based models, offering patterns for fine-tuning pretrained architectures for tasks such as text classification, named entity recognition, and question answering. It also includes a toolkit for model interpretability, allowing users

This repository provides a comprehensive educational framework for mastering machine learning and deep learning through a structured curriculum. It integrates theoretical mathematical foundations—including calculus, probability, and linear algebra—with hands-on laboratory implementations that require learners to build algorithms and neural network architectures from scratch. The project distinguishes itself by emphasizing first-principles development, ensuring that students understand the underlying mechanics of backpropagation, layer-wise computation, and model optimization. It covers a broa

This project is a Chinese translation of a comprehensive educational resource for implementing machine learning. It serves as a technical guide for developing machine learning models, providing translated documentation and practical tutorials. The resource focuses specifically on the implementation of machine learning using Scikit-Learn and TensorFlow. It provides guides for building traditional machine learning models as well as developing deep learning neural networks. The content covers the end-to-end machine learning workflow, including data preparation, model training, and evaluation. E

This project is a structured machine learning course and educational program designed to teach data analysis and gradient boosting. It consists of a ten-week curriculum that combines theoretical readings and videos with an interactive learning path. The material is delivered through a searchable documentation site and a course generator that produces book-formatted content for offline study. The curriculum integrates interactive notebooks, demo assignments, and competitive challenges to provide a practice environment for applying concepts to real-world datasets. The project utilizes a markdo

100-Days-Of-ML-Code is a machine learning curriculum and instructional resource designed as a structured 100-day learning path. It provides a sequence of daily milestones that cover the mathematical foundations and practical implementations of machine learning algorithms. The project is organized into specialized courses for supervised and unsupervised learning. Supervised learning materials cover the implementation of predictive models such as linear regression, decision trees, and support vector machines. Unsupervised learning materials focus on clustering models, including K-Means and hier

This repository serves as a machine learning educational archive and technical knowledge base. It provides a structured collection of study notes and documentation designed to assist learners in mastering fundamental machine learning algorithms, mathematical foundations, and predictive modeling concepts. The project functions as an open-source learning resource that facilitates collaborative knowledge management and educational archiving. By organizing complex technical topics into a searchable, hierarchical repository, it supports independent study and preparation for professional data scien

This project is a machine learning knowledge map and educational resource that provides a structured learning path for data science. It organizes core concepts, from basic data analysis to deep learning, into a visual guide and markdown-based knowledge graph. The resource connects theoretical foundations and mathematical concepts to practical execution through links to runnable notebooks and implementation examples. This allows for a transition from conceptual study to hands-on practice. The project uses hierarchical node organization and modular topic decomposition to visualize relationship

This project is an educational platform designed to teach artificial intelligence, neural networks, and data science through a combination of structured textbooks and interactive learning resources. It provides a comprehensive curriculum that guides students through sequential learning paths, bridging the gap between mathematical theory and practical software implementation. The platform distinguishes itself by integrating executable code environments and dynamic browser-based visualizations directly into its educational content. These tools allow users to modify model implementations in real

This project is a community-driven educational repository that serves as a comprehensive directory of university-level computer science video lectures. It provides a structured learning path for students and professionals, aggregating high-quality academic resources to facilitate self-paced study across a wide range of technical disciplines. The repository distinguishes itself through a collaborative maintenance model, utilizing version control workflows to allow contributors to expand and update the collection. Content is organized within a single, version-controlled document that leverages

This project is a community-driven technical translation effort and a machine learning educational resource. It focuses on the localization of official TensorFlow technical guides and deep learning concepts from English into Chinese. The project utilizes a distributed Git-based contribution workflow and a decentralized review process to manage the translation of complex software documentation. Content is authored using Markdown to maintain consistent formatting across different platforms. The repository organizes these translated guides within a nested folder hierarchy that mirrors the origi

This project is a comprehensive introductory guide to the Go programming language, translated into Chinese. It serves as a professional software engineering manual designed to help Chinese-speaking developers and students learn Go syntax and core concepts. The content is authored as markdown-based technical documentation. It utilizes a translation-layer mapping to align original English source material with the Chinese text to maintain conceptual accuracy. The repository employs a static site generation workflow and uses Git-based version control to manage the translated technical content.

This project is a documentation site for an AI coding assistant, providing technical guides and reference materials for writing and implementing software code. It is built as a markdown-based static site that delivers pre-rendered HTML for fast loading and simplified content authoring. The platform functions as a version-controlled documentation site, using a git repository to track content revisions and manage historical archives. It includes a client-side search index that loads a pre-computed JSON file into the browser to provide instant full-text search results. The content covers AI pai

This project is a software engineering playbook providing a collection of standardized guidelines and processes for managing the full software development lifecycle and team operations. It serves as a high-level framework for organizing agile project management, API design, containerized development standards, and markdown documentation workflows. The framework establishes a system for language-agnostic API design to automate client library generation and documentation. It also defines standards for providing uniform contributor environments and toolchains through virtualized containers. The

This project is an AI education resource consisting of synthesized learning materials designed for reviewing and mastering complex neural network concepts. It serves as a collection of curated course summaries and machine learning study notes that focus on the mathematical foundations and architectures of deep learning. The repository provides academic summaries and personal research insights specifically covering neural networks and sequence models. These materials are organized to support the review of theoretical foundations and the synthesis of core AI concepts. The content is stored as

SurviveSJTUManual is a university student guide and documentation website designed to provide academic planning and resource navigation for higher education. It serves as a student wellness resource and a comprehensive guide for meeting degree requirements and course selection. The project functions as a higher education career coach and international education planner, offering strategic advice on researching global degree programs, applying for international studies, and finding professional mentors. It also provides guidance on managing academic pressure and avoiding toxic environments to

This repository serves as the coordination hub for the Kubernetes community, focusing on open source contribution and project governance. It provides the structures necessary to manage the development of subprojects through a distributed governance model involving committees and working groups. The project manages community coordination by connecting contributors through a network of mailing lists and chat channels. It defines the requirements and responsibilities for contributor membership, including the process for becoming an official member or code reviewer. The repository utilizes a sta

js-the-right-way is a JavaScript best practices guide and coding standards reference designed to provide a curated collection of industry materials for writing maintainable code. It serves as a web development education resource, offering organized documentation on modern JavaScript patterns and idioms. The project is structured as a markdown-based documentation site, where guides written in lightweight markup are rendered as static pages. It utilizes a curated network of hyperlinks to connect internal documentation with external industry standards.

This project is a structured educational framework designed to guide developers through the core concepts of JavaScript programming and software engineering. It functions as a comprehensive training resource, providing a logical roadmap for mastering web development, from fundamental language syntax to full-stack application architecture. The platform utilizes a markdown-based documentation system that organizes technical learning materials into a clear, hierarchical curriculum. By employing a static site generator, the project transforms these plain-text educational modules into a collection

This project is a comprehensive educational resource and technical manual focused on interpretable machine learning and explainable AI. It serves as a textbook and reference for implementing techniques that make complex machine learning models transparent and understandable to humans. The resource provides guidance on both building inherently transparent models, such as decision trees and sparse linear models, and applying post-hoc explanation methods to black-box systems. It details specific methodologies for quantifying feature importance, generating rationales for individual predictions, a

This repository provides a collection of machine learning algorithms implemented from scratch using pure Python. It serves as an educational resource designed to demonstrate the internal logic and mathematical foundations of predictive models without relying on external machine learning frameworks or black-box libraries. The project distinguishes itself by mapping code implementations directly to their underlying statistical and calculus-based formulas. Each model is constructed using base language primitives and manual gradient descent optimization, allowing users to observe the mechanics of

This project is a comprehensive collection of machine learning educational resources, featuring a Python-based curriculum, study guides for deep learning, and a specialized knowledge base for machine learning operations. It provides structured learning paths that guide users from foundational programming through to advanced neural network implementations. The repository focuses on interactive learning by providing a directory of executable notebooks and cloud-hosted experiments. It maps theoretical research papers and textbooks to practical code implementations and maintains a curated directo

This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

This repository is a collection of machine learning course materials, providing study notes and Python implementation examples for a professional specialization. It serves as a guide for supervised and unsupervised learning, focusing on the application of fundamental algorithms. The content covers a broad range of machine learning education, including the mathematical foundations and practical prototyping of models. It specifically provides resources for implementing regression, classification, clustering, and dimensionality reduction techniques. The project is organized as a curriculum-base

This project is a machine learning course curriculum and educational resource repository. It serves as a centralized hub for accessing theoretical lecture notes, seminar materials, and practical homework assignments designed to teach machine learning fundamentals. The repository functions as an academic video archive, providing recorded university lectures and seminars to support self-paced technical learning and the archiving of historical academic records. The content is delivered via a static site generated from markdown files and organized through a flat-file information architecture.

ML for Hackers is a machine learning educational resource and library designed for learning the fundamentals of algorithmic programming and data analysis. It provides a neural network framework and a collection of mathematical implementations for building and training predictive models. The project utilizes a modular architecture for stacking linear transformations and activation layers. It implements core deep learning components from scratch using multi-dimensional arrays for tensor algebra and operations. The framework covers a variety of algorithmic capabilities, including automatic diff

This project is a curated directory of educational roadmaps and resource hubs for artificial intelligence, deep learning, and machine learning. It serves as a centralized collection of academic lectures, instructional videos, and courses designed to provide structured learning paths for AI practitioners. The directory covers specialized academic curricula across several core domains, including computer vision, natural language processing, and reinforcement learning. It also provides access to niche educational content such as medical imaging, Bayesian deep learning, and probabilistic graphica