The Little Book of ML Metrics is a comprehensive, collaboratively authored reference book that explains and visualizes evaluation metrics across the full spectrum of machine learning tasks: regression, classification, clustering, ranking, computer vision, NLP, generative AI, probabilistic estimation, bias/fairness, and data observability. The repository contains the LaTeX source code, a collection of Jupyter notebooks (and Python scripts) that generate all figures, and a set of custom tools for book production and review. Its primary function is to produce a high-quality, print-ready PDF that serves as both a pedagogical introduction and a practical decision guide for practitioners. The codebase is dominated by figure-generation scripts that implement each metric with synthetic or small real-world datasets and render Tufte-style, publication-quality plots. While the repository is not a traditional software application, it does include several bespoke tools: an interactive formula tuner (for adjusting TikZ arrow annotations on formulas), a review recorder (a local HTTP server for reviewing the PDF with audio notes), and a formula layout helper that auto-positions arrows. These tools are tightly integrated into the book production pipeline but are not intended for general reuse. The code is well-organized and follows consistent visual patterns (shared color palette, despine helpers, direct annotations), though there is considerable duplication across notebook scripts and a lack of automated tests. The LaTeX build process is robust but depends on a full TeX distribution and a third-party GitHub Action. Dependencies are pinned via pyproject.toml and uv.lock, and the genai figure generation requires a separate heavy environment (torch, transformers) run manually, which reduces full reproducibility out-of-the-box. Overall, this is a well-crafted book project with strong internal consistency, but its code is best viewed as supporting infrastructure rather than a standalone piece of software.
MiKTeX is a comprehensive, cross-platform TeX distribution that provides a complete typesetting system for Windows, macOS, and Linux. The repository contains the source code for TeX engines (pdfTeX, XeTeX, LuaTeX, e-upTeX, etc.), companion tools (dvipdfmx, dvisvgm, asymptote, metapost, tex4ht, etc.), the TeXworks editor, and the MiKTeX package manager (Console) and DVI viewer (Yap). It also bundles a large number of third-party libraries (FreeType, HarfBuzz, Poppler, LibreSSL, cURL, GMP, MPFR, etc.) as vendored dependencies. The architecture is monolithic: each executable compiles independently but shares a common core library (MiKTeX Core) that provides session management, file I/O, package management, and platform abstraction. The primary data flow is user-driven: packages are downloaded from remote repositories, TeX documents are compiled via one of the engines producing DVI or PDF output, and fonts are handled via system or bundled resources. The codebase is actively maintained with frequent releases, but the reliance on many vendored libraries and extensive platform-specific `#ifdef` blocks introduces moderate technical drift risk.
This repository is primarily an educational resource for teaching data science and machine learning concepts. It contains a extensive collection of Jupyter notebooks, Python scripts, and supporting materials covering topics from basic statistics and pandas to advanced deep learning, NLP, and LLMs. The content is organized as tutorials and examples rather than a cohesive application, with numerous datasets included for hands-on learning. The repository appears to be actively maintained with recent updates, suggesting efforts to keep the educational content current with evolving technologies.
LaResume is a full-stack, open-source resume builder and job application tracker built with Next.js (App Router), TypeScript, and Prisma (MongoDB). It provides multiple LaTeX-powered resume templates (sb2nov, techpro, deedy, mteck) with real-time preview, ATS optimization, and PDF export. The application includes a Kanban-style job tracker, shareable resume links with view analytics, and a one-time payment model for premium features (unlimited downloads, sharing). Authentication is handled by Clerk, and payments are processed via LemonSqueezy and Dodo Payments. The frontend uses shadcn/ui components, Redux Toolkit (RTK Query) for state management, and next-intl for internationalization across 12 languages. The backend exposes RESTful API routes for resume CRUD, sharing, compilation, and job board management, with LaTeX compilation running in a Dockerized TeX Live environment. The codebase also includes marketing pages (pricing, features, FAQ, contact) and SEO optimizations (JSON-LD, sitemap, Open Graph).
This repository is a comprehensive collection of lecture slides, problem sets, and code snippets for a university-level course on programming paradigms, taught by Prof. Edson Alves at Universidade de Brasília. It covers a wide range of paradigms including imperative (assembly, C, Fortran), functional (Haskell), logic (Prolog), object-oriented (Smalltalk), vector (APL), stack-oriented (Forth), and theoretical topics like lambda calculus, combinators, and Turing machines. The content is organized into topic-specific directories, each containing LaTeX slide decks (using custom classes `slides.cls` and `whiteboard.cls`) and corresponding code examples. While not a deployable application, the repository serves as a reusable template for creating similar educational materials, with consistent build scripts and shared LaTeX macros. The code examples are illustrative and tightly coupled to the slides, limiting standalone reuse. The repository shows signs of staleness (dependency on specific language versions, future-dated references, and no recent active development) but remains a valuable resource for educators in programming languages.
This repository is the public release of the SLAM Handbook, a comprehensive book on Simultaneous Localization and Mapping (SLAM) to be published by Cambridge University Press. It contains the book manuscript (main.pdf), LaTeX style files for acronyms and bibliography strings, and a bug report template. The repository is primarily a document project rather than a software application, but the LaTeX infrastructure (SH_acronyms.sty and SH_string.bib) serves as a reusable library for typesetting SLAM-related documents. The book covers foundations, practical implementations, and spatial AI, with contributions from numerous experts. The repository is actively maintained with incremental releases and community feedback mechanisms via issues and discussions.
This repository is a collection of web demos and examples for MathJax version 4, the JavaScript math rendering library. It demonstrates various ways to integrate MathJax into web pages, including standard TeX/MathML input with CHTML/SVG output, equation numbering and referencing, interactive reveal/toggle steps, speech generation, and compatibility with MathJax v2 configurations. The repo also contains a substantial custom font extension named 'mathjax-newcm' (New Computer Modern) that provides extensive glyph metrics and SVG path data for mathematical typesetting, with a specific focus on braille output (the 'euro-braille' subdirectory). This font data is split into dynamic chunks loaded on demand by MathJax. The codebase is primarily illustrative—each demo is a self-contained HTML page with accompanying JavaScript. The custom component and TeX extension examples show how to build tailored MathJax bundles using internal APIs. The configuration converter is a standalone utility that parses legacy MathJax v2 configuration objects and generates equivalent v4 configuration. While the demos are functional and well-organized, the font data files are minified/generated artifacts that are difficult to maintain manually, and several parts rely on MathJax internal APIs that may change across versions. The euro-braille and euro-braille-old directories contain near-duplicate font data with version drift, indicating a maintenance challenge. Overall, this repo serves as a practical reference for MathJax v4 integration patterns and custom font development, but it is not a production library itself. The most reusable components are the custom component assembly pattern, the TeX extension template, and the configuration converter utility.
This repository is the Chinese translation of the landmark Deep Learning textbook by Goodfellow, Bengio, and Courville. It contains the full LaTeX source of the translated book (20 chapters), a Jekyll-based website for online reading, and scripts to convert LaTeX into Markdown for the website. The repository also includes community annotations per chapter (hypothes.is) for proofreading and translation refinement. It is a static documentation project, not a software application or data pipeline, though it does have a build system (Makefile) and a web deployment pipeline. The translation is largely complete, but the content is based on the 2016 edition and has not been updated since 2019; the project is currently in a stale state with no active development.
This repository is a curated collection of prompt templates and agent skill configurations for AI-assisted academic writing. It contains no executable code, only a single README.md file that documents prompts for translation, polishing, logic checking, and figure/table caption generation, as well as instructions for configuring agent skills in tools like Cursor. The prompts are designed for use with large language models to help researchers write and refine papers. While the content is well-organized and practical, the repository is purely documentation with no software implementation, making it a reference resource rather than a functional application.