Projects sharing features with Csharp Notebooks

Eloquent-JavaScript is a comprehensive JavaScript programming textbook and interactive coding tutorial designed for web development education. It serves as both a language reference and a practical guide, combining theoretical lessons with an environment where learners can execute and modify code examples. The project focuses on the fundamental principles of the JavaScript language, including lexical scoping, prototype-based inheritance, and asynchronous patterns. It provides detailed instruction on object-oriented programming, functional programming, and the use of the browser DOM to create

This project is an interactive learning platform designed for mastering component-based user interface patterns and state management using the React library. It functions as a frontend development tutorial and course, providing a guided environment for learning declarative rendering and virtual DOM concepts. The platform distinguishes itself through a live-reload coding environment and a split-view interface for real-time code validation. These tools allow learners to compare their live code output against target solutions and use guided source-code markers to identify required edits and crit

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

react-live is a suite of tools for in-browser React transpilation, live editing, and interactive documentation. It provides a React component for building live playgrounds where source code is transpiled and rendered in real time, accompanied by a live code editor that offers syntax highlighting and immediate visual feedback. The project enables the creation of living documentation by embedding editable React code examples alongside their rendered output. It supports the execution of editable markup or functional components, allowing users to modify code and observe the resulting behavior ins

This project is a collection of programming language references and syntax cheat sheets designed for rapid developer onboarding. It serves as a library of code-based documentation that uses valid source code files to provide whirlwind tours of various language specifications. The project focuses on programming language learning by providing concise, commented code examples that explain core features and syntax in place. This approach enables developers to quickly grasp language-specific patterns, data types, and execution flow through a consistent reference format. The content covers a broad

Zen-C is a multi-target systems language and source-to-source compiler that translates high-level logic into human-readable GNU C or C11 code. It functions as a JIT-enabled programming language with an in-process compiler for real-time interactive code evaluation and testing. The project serves as a CUDA GPU kernel generator, mapping specialized syntax to CUDA C++ using device attributes to target graphics hardware. It acts as an interoperability layer capable of emitting compatible code for C++, Objective-C, and Lisp to bridge native system frameworks and libraries. The language includes an

This project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning. The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization. The curriculum covers broader capability area

This project is a collection of curricular resources and hands-on tutorials designed to teach Python programming and scientific computing. It consists of a series of interactive lessons and executable notebooks that provide a guided approach to learning Python through a combination of code and prose. The curriculum is specifically designed for experienced programmers to quickly master Python syntax, data structures, and core language semantics. It includes an introductory guide to the libraries and programming environments used for scientific computing and complex dataset analysis. The educa

Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution. The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex dif

This project is a collection of interactive Jupyter notebooks designed to teach machine learning and deep learning fundamentals through hands-on coding exercises. It provides a structured curriculum that guides users through the end-to-end data science lifecycle, covering everything from initial data preprocessing to final model evaluation. The repository distinguishes itself by bridging theoretical data science concepts with practical implementation using standard industry libraries. It features a series of tutorials that demonstrate how to build and train predictive models and complex neura

This project is a structured Rust programming course and learning path consisting of a sequence of interactive coding exercises. It provides a curriculum designed to guide learners from basic language fundamentals, such as syntax and type systems, to the development of complex applications. The learning experience is supported by a command-line solution validator that verifies the correctness of code solutions. The curriculum follows a progressive problem sequencing model, allowing users to master memory management and application logic through a series of practical coding challenges. The sc

This repository serves as an educational collection of Jupyter notebooks designed to demonstrate distributed data processing and machine learning workflows. It provides a structured resource for learning how to perform large-scale statistical analysis, execute relational queries, and develop predictive models using Python and Apache Spark. The project distinguishes itself by offering practical, interactive guides that bridge the gap between theoretical distributed computing concepts and applied data science. By utilizing notebook environments, it enables users to document and execute code for

tui.chart is a statistical charting engine and data visualization library designed to render interactive charts and graphs for complex numerical datasets. It functions as a responsive charting framework capable of producing various statistical representations, including line, bar, scatter, bubble, and treemap charts. The library distinguishes itself through an interactive visualization system that supports synchronized tooltips across multiple charts, range-based zooming, and data series selection. It includes a layout engine that automatically adjusts chart dimensions and animations based on

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

This is the companion code repository for the third edition of the book Python Machine Learning. It delivers the entire learning path as a structured collection of Jupyter notebooks that progress from classical machine learning algorithms to advanced deep learning models, with every concept demonstrated through executable code and narrative text. What distinguishes this resource is its pedagogical design. Each notebook cell encapsulates a single conceptual step, letting readers run, inspect, and modify discrete units of learning. The code provides interchangeable implementations of deep lea

This project is a collection of interactive Jupyter notebooks and a structured machine learning tutorial series. It serves as an educational resource for studying predictive modeling and statistical analysis through a curriculum of executable code examples. The notebooks are specifically designed to accompany video tutorials, integrating external video assets with live code to synchronize visual instruction with hands-on experimentation. This approach allows users to follow sequential lessons while executing and modifying machine learning workflows directly in a browser. The content covers t

This repository provides a collection of interactive Jupyter notebooks designed to bridge theoretical machine learning concepts with practical implementation. It serves as a structured educational curriculum for deep learning, offering hands-on tutorials that guide users through the fundamentals of neural network architectures and their application. The project distinguishes itself by demonstrating identical neural network architectures across multiple industry-standard machine learning libraries, allowing for direct comparison and framework-agnostic learning. It includes utilities to transfo

This project is a machine learning education resource consisting of Python implementations of statistical learning models and data analysis examples from a core textbook. It serves as a statistical modeling library that provides the code necessary to implement linear regression, classification, and unsupervised learning techniques for academic data analysis. The repository is structured as a reference-driven implementation, with a directory layout that mirrors the chapter and section hierarchy of the associated academic publication. It includes a set of scripts and notebooks designed to gener

This repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures. The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w

This project is a Python machine learning education kit that provides curated datasets and visualization scripts to teach fundamental machine learning concepts. It functions as both a machine learning visualization library and a collection of educational datasets designed for demonstrating and testing common models and patterns. The toolkit focuses on illustrating the internal logic and operational patterns of machine learning algorithms. It generates figures and datasets that visualize how different models behave and operate on data to aid in the learning process. The implementation utilize

This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

Polynote is a polyglot notebook environment and interactive document system designed for executing code in multiple languages within a single document. It functions as a cross-language data analysis tool and a JVM language IDE, allowing users to combine executable code, rich text, and data visualizations to prototype and document technical workflows. The system is distinguished by its ability to share data and variables between different language runtimes, such as Python and the JVM. It uses cross-language object conversion and data wrapping to pass objects between runtimes, enabling multi-la

This project is a comprehensive framework for literate programming that enables developers to build production-ready Python libraries entirely within Jupyter Notebooks. By treating notebooks as the primary source of truth, it integrates code, documentation, and testing into a unified development pipeline that exports directly to standard Python modules. The framework distinguishes itself through specialized tooling designed to overcome the inherent challenges of using notebooks in professional software engineering. It includes custom Git hooks and merge drivers that sanitize volatile notebook

Gridstudio is a web-based data science integrated development environment that combines a programmatic spreadsheet interface with an interactive Python environment. It functions as a system for organizing and deploying isolated data workspaces to handle data science tasks and storage. The platform merges spreadsheet data management with an execution engine for formulas and Python code, allowing for programmatic spreadsheet manipulation. It enables users to run interactive scripts and terminal sessions to clean, transform, and manage datasets within a browser. The environment supports Linux s

JupyterLab is a web-based development environment designed for interactive data science, collaborative research, and computational notebook authoring. It provides a unified workspace where users can execute code, manage computational kernels, and create documents that integrate live code, rich data visualizations, and narrative text. The platform is built on a modular architecture that supports extensive customization through a plugin system. This framework allows for the dynamic loading of extensions, enabling users to define custom file viewers, interface themes, and keyboard shortcuts. By

This project is a browser-based interactive computing environment and data science IDE. It serves as a literate programming tool that allows users to create documents combining live code, mathematical equations, visualizations, and narrative text. As a polyglot notebook interface, it connects to various language kernels to execute code and render output within a single interface. The application distinguishes itself by separating the frontend interface from a remote compute engine through a language-agnostic kernel interface. This allows it to support multiple programming languages while main

This project is an educational blog and learning resource dedicated to the Rust programming language. It provides a collection of curated guides, technical articles, and structured learning paths designed to teach language fundamentals, concurrency, and systems programming. The repository distinguishes itself by offering practical implementation tutorials for complex systems. This includes detailed guides on compiler development—specifically translating source code into targets such as ARM64, x86_64, LLVM IR, and WebAssembly—as well as networking examples for building multithreaded chat serve