Search Results for "common" - Page 8
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Showing 370 open source projects for "common"
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Anthony Fu's Skills
Anthony Fu's curated collection of agent skills
...The project serves as a curated registry of utilities that save time, standardize best practices, and encode expertise across domains, while still being easy to customize or extend. Contributors can add new skills following a common format, meaning the repository grows organically with community-driven capabilities. -
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Preline UI
Preline UI is an open-source set of prebuilt UI components
...Developers can quickly assemble complex, mobile-friendly user interfaces with consistent design and behavior straight out of the box, greatly reducing the overhead of crafting common UI patterns from scratch. Preline also offers setup guidance and integration examples so teams can get started rapidly within their Tailwind projects. Because it follows Tailwind’s conventions, it integrates smoothly with other Tailwind-first tools and workflows, maintaining a lean stylesheet footprint and maximizing flexibility. -
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ESPnet
End-to-end speech processing toolkit
ESPnet is a comprehensive end-to-end speech processing toolkit covering a wide spectrum of tasks, including automatic speech recognition (ASR), text-to-speech (TTS), speech translation (ST), speech enhancement, speaker diarization, and spoken language understanding. It uses PyTorch as its deep learning engine and adopts a Kaldi-style data processing pipeline for features, data formats, and experimental recipes. This combination allows researchers to leverage modern neural architectures while... -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. ... -
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
...The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). ... -
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Java Telegram Bot API
Telegram Bot API for Java
...A bot can offer rich HTML5 experiences, from simple arcades and puzzles to 3D-shooters and real-time strategy games. Build social services. A bot could connect people looking for conversation partners based on common interests or proximity. -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their desired customizability from a smooth range: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space, and metric), or full customization (arbitrary training and evaluation code). ... -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. ... -
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Kong
The Cloud-Native API Gateway
Kong is a next generation cloud-native API platform for multi-cloud and hybrid organizations. When building for the web, mobile, or Internet of Things, you’ll need a common functionality to run your software, and Kong is that solution. Kong acts as a gateway, connecting microservices requests and APIs natively while also providing load balancing, logging, monitoring, authentication, rate-limiting, and so much more through plugins. Kong is highly extensible as well as platform agnostic, connecting APIs across different environments, platforms and patterns. ... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
...With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
...It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. Explanations emphasize intuition first, then key formulas and common pitfalls, so you can reason through unseen questions rather than memorize trivia. Many entries connect theory to implementation details, including how choices in activation, initialization, or normalization affect convergence and stability. The content is organized for fast review before an interview loop but is also deep enough for systematic study over weeks. ... -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
...The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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Machine Learning Notebooks
Machine Learning Notebooks
Machine Learning Notebooks is an open-source collection of machine learning notebooks designed to provide practical, minimal, and reusable implementations of common AI tasks across different domains. The project focuses on delivering concise, well-structured Jupyter notebooks that demonstrate how to build, train, and evaluate models using modern machine learning frameworks such as PyTorch. Each notebook is intentionally lightweight, avoiding unnecessary complexity so that users can easily understand the core concepts and adapt the code to their own projects. ... -
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ProjectLibre - Project Management
#1 alternative to Microsoft Project : Project Management & Gantt Chart
ProjectLibre project management software: #1 free alternative to Microsoft Project w/ 7.8M+ downloads in 193 countries. ProjectLibre is a replacement of MS Project & includes Gantt Chart, Network Diagram, WBS, Earned Value etc. This site downloads our FOSS desktop app. 🌐 Try the Cloud: http://www.projectlibre.com/register/trial We also offer ProjectLibre Cloud—a subscription, AI-powered SaaS for teams & enterprises. Cloud supports multi-project management w/ role-based access, central... -
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Practical Machine Learning with Python
Master the essential skills needed to recognize and solve problems
...It centralizes example code, datasets, model pipelines, and explanatory notebooks that teach users how to approach problems from data ingestion and cleaning all the way through feature engineering, model selection, evaluation, tuning, and production-ready deployment patterns. The repository emphasizes end-to-end workflows rather than isolated code snippets, showing how to handle common challenges like class imbalance, overfitting, hyperparameter optimization, and interpretability. By leveraging popular Python libraries such as pandas, scikit-learn, XGBoost, and visualization tools, it illustrates how to build reproducible and robust solutions that scale beyond small demos. -
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Data Annotator for Machine Learning
Data annotator for machine learning
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. User management panel with role-based access control. -
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snntorch
Deep and online learning with spiking neural networks in Python
...This allows researchers to train spiking neural models using familiar deep learning workflows while taking advantage of GPU acceleration and automatic differentiation. snnTorch provides implementations of common spiking neuron models, surrogate gradient training methods, and utilities for handling temporal neural dynamics. Because spiking neural networks operate over time and encode information through spike timing, the library includes tools for simulating temporal behavior. -
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AI File Sorter
Local AI file organization with categorization and rename suggestions
...The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common text files. For supported audio and video files, AI File Sorter can read embedded metadata (such as ID3, Vorbis, and MP4 tags) to suggest normalized names like year_artist_album_title.ext. AI analysis runs read-only, and all suggestions must be reviewed before being applied. AI File Sorter can run fully offline using local models like Mistral or LLaMA, so files and metadata stay on your device unless you configure a remote endpoint. -
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OpenAI Quickstart Python
Python example app from the OpenAI API quickstart tutorial
...It provides practical, beginner-friendly examples to help developers quickly learn how to send requests, handle responses, and build basic applications using the OpenAI Python SDK. The examples folder includes small, self-contained projects showcasing common use cases like chat completions, tool usage, and interactive interfaces. Each example is designed to be easily runnable with minimal setup—requiring only Python, a virtual environment, and an API key. The repository also includes environment setup guides and example scripts, such as a simple Flask web app for chat interactions, allowing developers to test OpenAI API integrations locally. ... -
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Hiera
A fast, powerful, and simple hierarchical vision transformer
Hiera is a hierarchical vision transformer designed to be fast, simple, and strong across image and video recognition tasks. The core idea is to use straightforward hierarchical attention with a minimal set of architectural “bells and whistles,” achieving competitive or superior accuracy while being markedly faster at inference and often faster to train. The repository provides installation options (from source or Torch Hub), a model zoo with pre-trained checkpoints, and code for evaluation... -
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LIDA
Automatic Generation of Visualizations and Infographics using LLMs
...Instead of requiring users to manually explore datasets and write plotting scripts, LIDA analyzes the data and automatically proposes visualization goals and design ideas that highlight patterns and relationships. The platform can generate visualization code compatible with a wide range of libraries, allowing it to integrate with common data science ecosystems. It also supports iterative workflows where visualizations can be edited, explained, evaluated, and repaired through AI-driven feedback loops. The system is model-agnostic and can connect to multiple language model providers, enabling flexibility across different AI infrastructures. -
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ModelFusion
The TypeScript library for building AI applications
...The framework allows developers to integrate large language models and other generative systems into JavaScript and TypeScript applications through a consistent and standardized API. Instead of writing separate integration logic for each provider, developers can use ModelFusion to handle common operations such as text generation, structured object generation, streaming responses, and tool calls. The library supports a wide range of model types, including text generation models, vision models, text-to-speech engines, speech-to-text systems, and embedding models. It also includes built-in production features such as observability hooks, logging, automatic retries, and error handling mechanisms that improve reliability when deploying AI systems in real-world environments. -
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OpenChat for Linux
OpenChat for Linux — a fast, lightweight desktop client for ChatGPT
...It’s built with Tauri (Rust) for low resource usage and stability, and it uses a “message window” approach (keeps a small active slice of the conversation and loads more as you scroll) so long chats don’t bog down or crash the app. Downloads are available in common Linux formats (AppImage, Debian package, tarball), with additional packaging manifests for Flatpak, Snap, RPM, AUR, and Nix. -
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Arthur Bench
Bench is a tool for evaluating LLMs for production use cases
Bench is a tool for evaluating LLMs for production use cases. Whether you are comparing different LLMs, considering different prompts, or testing generation hyperparameters like temperature and # tokens, Bench provides one touch point for all your LLM performance evaluation. -
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DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT)...
