Projects sharing features with Data Science Cheatsheet

This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr

This project is a machine learning textbook companion and code reference that translates theoretical statistical learning exercises into executable implementations. It serves as a programmatic study guide for implementing foundational machine learning algorithms and solving structured data problems. The repository provides predictive modeling notebooks that combine narrative explanations with code to derive and validate statistical algorithms. These implementations are available as a reference for both Python and R, utilizing the Scikit-Learn API for model fitting and prediction. The codebas

This project is a curated knowledge repository providing theoretical guides, practical challenge banks, and professional handbooks for technical interview preparation in data science and machine learning. It serves as a comprehensive study resource that combines theoretical knowledge with algorithmic practice. The repository features specialized study resources including a probability and statistics handbook, a machine learning reference for algorithms and neural network architectures, and a coding and SQL challenge bank designed to simulate recruitment assignments. It also includes a technic

This project is a technical learning resource and algorithm reference guide consisting of pedagogical study notes on machine learning. It provides academic summaries and conceptual breakdowns designed to help students navigate comprehensive machine learning textbooks. The content is structured as a collection of notes covering the theoretical foundations and implementation logic of supervised, unsupervised, semi-supervised, and reinforcement learning algorithms. It focuses on the mathematical foundations and logic behind various algorithmic approaches to solving data problems. The resource u

This project is a machine learning educational resource and study site focused on the theoretical foundations and mathematical derivations of machine learning algorithms. It serves as a study guide for mastering the linear algebra, calculus, and proofs required for predictive modeling. The site functions as a markdown documentation portal and static site generator, converting formatted text and LaTeX formulas into a structured web interface. It utilizes a typesetting engine to render complex academic derivations and mathematical equations clearly within the browser. The platform includes a r

This repository is a collection of machine learning theory notes and mathematical references. It serves as a structured study guide containing conceptual explanations and handwritten mathematical derivations of the foundations and core formulas used in the field. The content focuses on the mathematical derivation of algorithms, breaking down the step-by-step logic and proofs required to understand their inner workings. These academic records utilize typesetting for precise scientific notation and mathematical documentation. The materials are organized as a markdown-based study guide with a t

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

This project is a machine learning reference guide and condensed cheat sheet providing a curated collection of classical equations, diagrams, and core concepts. It serves as a technical interview study guide focused on the mathematical foundations and theoretical principles required for machine learning engineering roles. The resource facilitates the review of algorithm theory and data science interview preparation by offering a centralized location to recall fundamental machine learning patterns and mathematical proofs. It functions as a study guide for academic exams and a quick-reference t

This project is a machine learning curriculum and data science educational resource. It provides a structured set of instructional materials and hands-on projects designed for learning machine learning concepts and the implementation of predictive models. The resource functions as a training guide for supervised learning, focusing on the development of models for image classification and digit recognition. It uses a project-based training approach that pairs theoretical lessons with dataset-driven model training and evaluation. The curriculum covers the mathematical foundations of machine le

This is a Chinese-language technical interview preparation resource focused on algorithms and data structures. It compiles real-world written exam questions and interview experiences to provide practical, scenario-specific guidance for candidates preparing for technical assessments. The content is organized into distinct topic modules covering machine learning, deep learning, computer vision, natural language processing, and mathematics. Each module reviews core concepts, architectures, and techniques commonly addressed in interview questions, with explanations curated around actual assessmen

This repository provides a comprehensive academic curriculum for machine learning and artificial intelligence. It serves as a structured educational framework, offering a collection of lecture materials and practical exercises designed to guide learners through the fundamental concepts and mathematical foundations of statistical modeling. The curriculum is delivered through interactive notebooks that combine explanatory text with executable code, allowing for real-time experimentation with algorithms. The content is organized into a modular hierarchy that separates theoretical instruction fro

The data engineering wiki is a crowdsourced knowledge base and reference guide assembled through collaborative contributions from practitioners. It functions as a structured repository of learning paths, architectural decision guides, and software evaluations for data systems, compiled from plain-text source markup files into a searchable static documentation site. The content is organized into strict conceptual hierarchies covering core engineering concepts, security and governance, and infrastructure tools. Contributors and readers can explore foundational architectural patterns, storage s

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

embedded-notes is a collection of technical study guides and development notes focused on embedded Linux, Linux kernel internals, and C programming. It serves as a reference for embedded systems development and a preparation resource for technical interviews in the field. The project provides detailed documentation on writing device drivers, managing virtual memory, and understanding kernel internals. It also includes guides on IoT network protocols, such as MQTT and TCP/IP, and outlines the architectural details of chip architectures and hardware peripherals. The material covers a broad sur

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 repository serves as a comprehensive educational resource for machine learning, providing a structured collection of lecture notes and reference materials. It covers the fundamental mathematical and statistical principles required to build, evaluate, and optimize predictive models, ranging from basic probability and linear algebra to advanced algorithmic implementations. The content is organized through a hierarchical mapping of concepts that connects mathematical prerequisites to specific machine learning theories. It features a modular design that segments complex topics into discrete,

Storm is an automated research platform that coordinates multiple language model agents to conduct internet-based information gathering and generate structured, citation-backed articles. The system functions as a modular framework that grounds generated content in real-time web data, ensuring that all outputs are verifiable and evidence-based. The platform distinguishes itself through a multi-agent discourse orchestrator that simulates expert dialogues to refine information discovery. By utilizing hierarchical concept mapping, the system organizes retrieved data into dynamic structures, allow

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 computer science exam study resource and academic knowledge base. It serves as an exam preparation toolkit providing curated textbooks, past exam papers, and study guides specifically for graduate entrance exams. The repository functions as an algorithm and data structures reference library, containing source code and implementation guides for core computer science fundamentals. It organizes conceptual notes, presentation slides, and mind maps for subjects including operating systems and computer organization. The project is implemented as a markdown-based knowledge base an

InterpretableMLBook is a comprehensive Chinese translation of the guide to understanding and explaining black-box machine learning models. It serves as a technical reference and manual for applying model-agnostic techniques to interpret the internal logic of complex algorithms. The resource focuses on black-box model analysis, providing a systematic approach to explaining individual predictions using methods such as Shapley values and LIME. It covers the evaluation of different interpretation methods to determine the most appropriate technique for a given project. The content is organized in

Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th

53AIHub is a centralized orchestration platform for deploying and managing AI agents and prompts across multiple large language model providers. It functions as a multi-model AI gateway and an operation portal for AI services, providing a unified interface to coordinate agents and prompts from various external platforms. The project distinguishes itself as a white-label AI portal designed for self-hosted infrastructure, allowing for full control over operational data on private servers or containers. It includes a comprehensive AI SaaS administration layer with a multi-tenant subscription eng

gbrain is an agent framework and retrieval-augmented generation system that combines a durable task queue, a git-synced vector store, and a knowledge graph engine. It provides a foundation for building AI agents that interact with structured knowledge bases using the Model Context Protocol. The system synchronizes markdown files from a git repository into a database for high-performance semantic retrieval and creates typed edges between data pages by extracting entity references and wikilinks. It uses a database-backed queue to execute persistent background jobs and tool loops, ensuring relia

DeepResearch is an autonomous research agent framework designed to orchestrate multi-step information gathering and complex reasoning tasks. The platform functions as an agent orchestration system that manages the entire lifecycle of autonomous research, from initial planning and web navigation to the synthesis of evidence-backed reports. The framework distinguishes itself through a specialized training pipeline that supports the development and fine-tuning of autonomous models using reinforcement learning and structured knowledge graph synthesis. By employing parallel agent coordination, the

This repository is a comprehensive resource for software engineering career development and technical interview preparation. It provides a structured collection of learning materials, algorithmic patterns, and system design guides designed to assist developers in mastering the core competencies required for professional engineering roles. The project distinguishes itself through a pattern-based content taxonomy that groups diverse technical challenges by underlying algorithmic strategies. This approach allows users to identify and apply reusable solutions during high-pressure assessments. It

Open Deep Research is an artificial intelligence framework designed to automate complex, multi-step research workflows. It functions as an autonomous agent that performs iterative web searches, analyzes retrieved data, and synthesizes information into structured reports. By decomposing broad queries into smaller sub-tasks, the system builds a comprehensive knowledge base to address open-ended questions. The platform distinguishes itself through an agentic loop that dynamically refines research strategies based on previous findings. It manages long-form data by compressing and summarizing cont

This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that