This repository serves as the companion code for the O'Reilly book 'Practical MLOps', providing a collection of examples spanning the MLOps lifecycle. It is a hybrid repository with a dominant data research character, containing Jupyter notebooks for exploratory data analysis (e.g., COVID-19 trends, sugar intake correlations), AutoML experiments (Ludwig text classification), and model interoperability scripts (ONNX conversion). It also includes application-like components such as a Flask-based model serving container, CLI tools for CSV linting, and cloud deployment scripts for AWS, Azure, and GCP. The code is organized by book chapters and demonstrates practical MLOps patterns, though many examples are simplified for educational purposes rather than production readiness. The most reusable asset is the modularized CSV linter, which provides a well-structured CLI for data quality checks.
This repository implements an end-to-end predictive modeling pipeline for Premier League football match outcomes (home win, draw, away win). It ingests raw match statistics from the api-football.com REST API, processes JSON data into structured DataFrames, engineers features using rolling averages (5- and 10-game windows) and team-vs-opponent differentials, trains multiple classifiers (Random Forest, SVM, KNN, MLP) with hyperparameter tuning, and serves live predictions via a Flask web application. The dominant character is data research: the primary deliverable is a set of trained models and an analytical pipeline, with the web server being a secondary deployment artifact. The best-performing model is a Random Forest achieving ~50-51% test accuracy, demonstrating that football outcomes are modestly predictable from historical statistics. The codebase includes modular helper modules for feature engineering, data scaling, and evaluation plotting that are reusable for other sports or time-series classification tasks. However, the project suffers from hardcoded absolute paths, outdated dependencies (Python 3.7, sklearn 0.23.1), plain-text API keys in the repository, and a static dataset that will drift over time.
This repository is a curated resource list for deep learning-based face restoration algorithms, organized as a survey. It contains no executable code, data processing scripts, or model implementations. Instead, it provides structured tables categorizing research papers by year, publication, title, links, and architecture across tasks like blind face restoration, super-resolution, deblurring, denoising, and artifact removal. The README.md serves as the primary documentation, supplemented by illustrative GIFs in the figures directory. While valuable as a reference, it lacks any reusable components or reproducible research elements since it solely aggregates external links without original code, data, or experiments.
EmbedBooks is a Chinese-language, static GitHub repository whose only substantive content is README.md: a hand-maintained, categorized index of embedded-systems and computer-science textbooks, mostly PDFs hosted on Baidu Pan. It organizes 80+ titles into categories such as electronics, C, data structures, OS, ARM/RISC-V, Linux, networking, testing, design patterns, and career material. Every entry consists of a book title, an external Baidu cloud link, and an extraction code. There is no application code, no build system, no CI, no tests, and no executable logic.
This repository contains the reference implementation of AugMix, a data augmentation technique for improving robustness and uncertainty calibration in image classification models. It provides training scripts for CIFAR-10/100 and ImageNet using PyTorch, along with evaluation on their corrupted versions (CIFAR-C, ImageNet-C). The core augmentation logic is implemented in NumPy and PIL, and the training code applies a Jensen-Shannon Divergence consistency loss between augmented and clean images. The codebase is research-oriented, with scripts that are well-structured but heavily tied to the specific experimental setup of the paper. Dependencies are pinned but severely outdated (torch==1.2.0), making reproduction on modern systems challenging without adaptation.
This repository is a static, curated directory of Telegram resources—groups, channels, bots, VPNs, cryptocurrency exchanges, and other utilities. The entirety of the codebase is a single, extensive README.md file organized into clear sections with navigation anchors. There is no application code, data pipeline, or analytics logic. The repo serves as a reference index for Telegram users, particularly the Chinese-speaking community. Its value lies in the comprehensiveness and ongoing maintenance of the link collection, not in any software architecture or data engineering. Clone reproducibility is not applicable as there are no data transformations or analytical outputs to replicate.
This repository is a curated list of free resources and a roadmap for learning Machine Learning and Artificial Intelligence, primarily consisting of markdown files with external links. It categorizes resources such as key concepts, building blocks, courses, certifications, books, tools, research blogs, interview prep materials, newsletters, and project ideas. The repo contains no executable code, data pipelines, or software applications; it serves as a structured guide rather than a functional project. Its value lies in being a well-organized reference for learners, but it offers no reusable software components, data assets, or reproducible research.
This repository contains the research code for UniDriveVLA, a unified Vision-Language-Action (VLA) model for autonomous driving. It couples a Qwen3-VL based understanding expert with sparse 3D perception and trajectory planning experts via a Mixture-of-Transformers design. The codebase supports two parallel experiment tracks: nuScenes (real-world open-loop evaluation) and Bench2Drive (CARLA closed-loop simulation). It includes full training pipelines (staged pretraining on driving QA datasets, then fine-tuning on nuScenes/Bench2Drive), evaluation harnesses for detection, motion, planning, and VQA, and a CARLA leaderboard integration for scenario-based testing. The repository is primarily a research artifact—it is not a production application but rather a comprehensive release of model definitions, training scripts, data converters, and evaluation metrics for reproducing the paper's results. Significant code duplication exists between the nuScenes and Bench2Drive directories, and many paths are hardcoded, reducing portability. The code demonstrates strong engineering for research (custom CUDA ops, distributed training with DeepSpeed, modular mmdet3d plugins) but lacks the polish of a reusable library.
This repository is a data collection and publishing pipeline for Product Hunt daily launches. It ingests data from the Product Hunt API (via an external consumer app identified by the UTM campaign 'decohack') and produces one markdown file per day, with each file containing structured entries for products: title, tagline, description, keywords, vote counts, comment counts, featured status, and links. The markdown files are the primary data artifact and are consistently formatted, making them parseable for trend analysis, competitive research, or AI training corpora. Supporting Python scripts handle image repair (with exponential backoff for rate limits), WordPress publishing via REST API, and markdown generation. GitHub Actions workflows automate generation, image fixing, and publishing. The repository is best classified as a data pipeline rather than an application: it exposes no API endpoints, UI, or library interface, and its value lies in the accumulated dataset and the automation around it. However, it is a hybrid in that it contains application-like scripts and CI/CD workflows. The data lineage is straightforward: Product Hunt API responses are transformed into daily markdown files, then optionally enriched with images and published to WordPress. The dataset in the analyzed chunk is incomplete (only a subset of dates is present), and there is no schema enforcement, validation, or test suite. The pipeline depends on external services (Product Hunt API, OpenAI, WordPress) that may change or rate-limit, and the hardcoded OpenAI model (gpt-4o-mini) is a drift risk. The markdown format is human-readable but less machine-friendly than JSON/CSV, and no README or data dictionary documents the schema or provenance. Although the last commit is recent, the data files in the analyzed chunk stop at 2025-09-28, suggesting the ingestion pipeline may not be running reliably.
This repository is an educational book/course (DataWhale 'diy-llm') that teaches building and training LLMs from scratch. It spans the full pipeline: tokenizer, Transformer architecture, GPU high-performance programming, scaling laws, data engineering, distributed training, alignment (including GRPO/RLVR), evaluation, and multimodal models. The content is primarily Chinese, with Jupyter notebooks and code snippets embedded in markdown chapters. It is not a production library or application; it is a tutorial/textbook with homework assignments.
This repository is a Jupyter Book titled 'Learning Machine', providing concise, intuitive explanations of machine learning and deep learning concepts. It is organized into sections covering basics, common tasks, layers, generative models, reinforcement learning, and more. The content is primarily textual with illustrative code snippets using numpy, scipy, and PyTorch. No real datasets are used; all examples are synthetic. The book is designed as a quick-reference handbook for learners, accompanying a university course. The repository is effectively archived by the author, with no planned updates.
This repository is a long-running weekly newsletter archive (OSDT Weekly) for the Chinese open-source compiler and toolchain community. It aggregates and summarizes upstream development activity from GCC, LLVM, Binutils, GDB, glibc, QEMU, RISC-V, RT-Thread, and AI compiler projects, along with job postings and community event announcements. The content is entirely hand-curated Markdown files organized by year and date, with no executable code, build scripts, or automated pipelines. While it serves as a valuable historical record and community hub, it is not a software application or a traditional data research project with reproducible pipelines. The repository is actively maintained with weekly updates extending into 2026, but its reproducibility is low due to the lack of dependency management, structured data sources, or environment definitions.
This repository is the companion code for the first edition of 'Hands-on Machine Learning with Scikit-Learn and TensorFlow' (2017). It contains a collection of Jupyter notebooks teaching fundamental and advanced machine learning concepts, covering classification, SVMs, decision trees, ensemble methods, dimensionality reduction, and deep learning with TensorFlow 1.x (including CNNs, RNNs, autoencoders, and reinforcement learning). The code is educational, with step-by-step implementations, inline explanations, and exercise solutions. It is not a production application but a self-contained learning environment, supplemented with datasets (California housing, OECD life satisfaction, MNIST) and a Docker setup for reproducible execution. Significant technical drift exists: TensorFlow 1.x APIs, deprecated scikit-learn imports, and Python 2/3 compatibility shims make the notebooks incompatible with modern environments without substantial rewriting. The README explicitly directs users to the second edition repository for up-to-date code.
This is the research archive for the Hyperspace P2P network, a distributed AGI system. The repository primarily contains documentation, leaderboards, trained model weights, and configuration files for autonomous research agents. It serves as a durable record of experiments run by agents across 5 research domains (machine learning, search, finance, skills, causes). The main outputs include a collaboratively trained language model (qwen2.5-0.5b-hyperspace-v1) trained by 32 anonymous nodes using DiLoCo + SVD compression, and live leaderboards updated via GitHub Actions. While the repo itself is mostly static artifacts, it references a larger codebase (the Hyperspace CLI and P2P stack) that is not included. The repository is hybrid: primarily a research data archive, but also includes a standalone CLI tool (agentboard) for a blockchain-based message board.
This repository contains purely documentation describing a zero-click iOS exploit chain targeting CVE-2025-31200 (CoreAudio heap corruption) and CVE-2025-31201 (AppleBCMWLAN kernel escalation), with an additional unpatched hardware vector via BCM4387 coexistence SRAM. The content is limited to two Markdown files: a detailed technical walkthrough (Attack Chain Flow.md) and a summary README. There is no executable code, data, scripts, or configuration present. The repository serves as a research write-up detailing the attack flow, delivery mechanism, post-exploitation behavior, and mitigation recommendations. It does not contain any reusable data assets, pipelines, or analytical components since it lacks ingested data, transformation logic, or models.
This repository contains the research code for Video-SALMONN-2, a family of audio-visual large language models for video understanding, developed by Tsinghua University and ByteDance. It includes three generations: Video-SALMONN-2 (based on LLaVA), Video-SALMONN-2+ (based on Qwen2.5-VL), and Video-SALMONN-2 Pro (based on Qwen3-VL). The core contribution is a caption-enhanced training paradigm that tightly aligns audio and visual signals. The codebase provides full training pipelines for supervised fine-tuning (SFT) and direct preference optimization (DPO), along with evaluation scripts, a demo mode, and a vLLM-based serving plugin for deployment. While the project is predominantly research-focused, it includes application-like components such as CLI inference and OpenAI-compatible serving. The code exhibits high duplication across the three model variants, hardcoded paths to internal cluster storage, and brittle dependency pinning, making it challenging to reproduce outside the original environment. However, several components—such as the multimodal batch sampler, audio-video processing utilities, and QFormer-based fusion—are well-structured and potentially reusable for other multimodal research projects.
This repository is an educational cookbook from Upstage, centered on their Solar LLM and Document AI APIs. It contains a large collection of Jupyter notebooks (Solar-Fullstack-LLM-101) that teach prompt engineering, RAG, cache-augmented generation, tool calling, and fact-checking with knowledge graphs, plus a Flask sample backend (Bremen) and a Flutter mobile app. The dominant character is a notebook-driven ML/research collection, but it is hybrid because it includes runnable application code (Flask API, Flutter app) and static data assets (OCR JSON, CSV knowledge bases).
This repository serves as a curated directory of GitHub projects focused on 'human distillation skills'—the practice of extracting and encoding human expression styles, decision-making frameworks, and interaction patterns from individuals (such as personal contacts, idols, or public figures) into AI-readable formats. It does not contain any executable code, data processing pipelines, or analytical models itself; instead, it functions as a community-maintained index linking to external repositories that implement specific skill sets. The project is organized into thematic categories (self, interpersonal relationships, idols, and niche communities), each containing a markdown-formatted table of linked repositories with concise descriptions. Contributions are accepted via pull requests following a strict format to ensure consistency and usability. While the repository lacks traditional software engineering artifacts, it provides value as a discovery tool for researchers, developers, and enthusiasts seeking pre-existing work in the niche domain of personality-driven AI skill development. From a data perspective, the repository's sole asset is its README.md file, which acts as a structured dataset of skill project metadata. However, it does not include any data transformation, validation, or analytical components, limiting its reusability as a data engineering asset. The project exhibits minimal technical drift due to its simplicity and absence of external dependencies, and its maintenance status appears maintained based on recent commit activity (last commit 2026-04-08) and zero open issues, indicating ongoing community stewardship without significant backlog.
This repository is a curated list of open-source sysadmin tools, functioning as a knowledge base rather than an executable application. It contains no source code, API endpoints, or data processing pipelines. The content consists entirely of Markdown documentation linking to external software projects across categories like backups, monitoring, configuration management, and virtualization. The PR template explicitly redirects contributions to a separate repository (`awesome-sysadmin-data`), indicating this repo may serve as a mirror or legacy interface while the active data management occurs elsewhere. The repository structure is flat, containing only documentation files (README.md, LICENSE.txt) and GitHub issue templates. There are no executable components, unit tests, or dependency manifests. While the content is valuable for discovery, it lacks the engineering substance required for software reuse. The metadata (links, descriptions, licenses) is static and human-curated, making it a dataset of software recommendations rather than a data pipeline. The repository is actively maintained based on the provided commit history (2026-06-20), though the workflow is split between this repo and the `-data` variant. Technical drift is minimal as Markdown content does not degrade, though external links may rot over time. Reproducibility is high as the content is static and fully documented within the file itself.
This repository implements multiple state-of-the-art Visual Question Answering (VQA) models from 2018, including bottom-up top-down attention, Bilinear Attention Networks (BAN), inter-intra modality attention, learning to count, and conditioned graph structures. It is built on Cyanogenoid's vqa-counting framework and provides a clean, modular codebase for training and evaluating VQA models on the VQA v2 dataset. The code includes data preprocessing scripts (feature extraction, vocabulary building), a flexible training pipeline with various loss functions and learning rate schedules, and evaluation scripts that compute per-category accuracy. While the code is well-structured for research purposes and contains reusable components like the counting module and fusion layers, it relies on deprecated PyTorch APIs (e.g., Variable), hardcoded paths, and lacks unit tests or environment definitions, making reproducibility challenging. The repository is primarily a research artifact for comparing attention mechanisms and counting strategies in VQA.