#python #ai #artificial_intelligence #cython #data_science #deep_learning #entity_linking #machine_learning #named_entity_recognition #natural_language_processing #neural_network #neural_networks #nlp #nlp_library #python #spacy #text_classification #tokenization

spaCy is a powerful tool for understanding and processing human language. It helps computers analyze text by breaking it into parts like words, sentences, and entities (like names or places). This makes it useful for tasks such as identifying who is doing what in a sentence or finding specific information from large texts. Using spaCy can save time and improve accuracy compared to manual analysis. It supports many languages and integrates well with advanced models like BERT, making it ideal for real-world applications.

https://github.com/explosion/spaCy

#python #ai #big_model #data_parallelism #deep_learning #distributed_computing #foundation_models #heterogeneous_training #hpc #inference #large_scale #model_parallelism #pipeline_parallelism

Colossal-AI is a powerful tool that helps make large AI models faster, cheaper, and easier to use. It uses special techniques like parallelism to speed up training on big models without needing expensive hardware. This means users can train complex AI models even on regular computers or laptops, saving time and money. Colossal-AI also supports various applications across industries like medicine, video generation, and chatbots, making it very versatile for developers.

https://github.com/hpcaitech/ColossalAI

#python #agent #ai #automation #data_mining #data_science #development #llm #research

RD-Agent is a tool that helps automate research and development (R&D) tasks. It can read reports, propose new ideas, and implement them using data. This tool acts like a copilot for researchers, automating repetitive tasks or working independently to suggest better solutions. RD-Agent supports various scenarios, such as finance and medical fields, making it easier to streamline model development and data analysis. By using RD-Agent, users can save time and boost productivity in their R&D work.

https://github.com/microsoft/RD-Agent

#shell #chart #charts #d3 #data_visualization #svg #visualization

D3.js is a free JavaScript library that helps create interactive and custom data visualizations. It offers great flexibility, allowing developers to make dynamic graphics using web standards. This means you can easily customize how your data looks and behaves on the web. The benefit of using D3.js is that it allows users to explore complex data in an engaging way, making it easier to understand and analyze information. Whether you're working with simple charts or complex networks, D3.js provides tools to bring your data to life interactively.

https://github.com/d3/d3

#go #cicd #data_masking #data_security #database_access #dbeaver #devsecops #flyway #gitops #liquibase #mongodb #mysql #oracle #pam #postgresql #schema_migrations #security #snowflake #sql_client #sqlserver #tidb

Bytebase is a tool that helps manage databases more efficiently. It automates tasks like schema migrations and backups, making it easier to collaborate on database changes. Bytebase supports multiple databases and integrates well with CI/CD pipelines. It also provides features like role-based access control and data masking for better security. Using Bytebase simplifies database management, reduces errors, and saves time, making it beneficial for developers and database administrators.

https://github.com/bytebase/bytebase

#typescript #data_visualization #database #database_schema #documentation #documentation_tool #entity_relationship_diagram #er_diagram #erd #nodejs #orm #postgresql #prisma #react_flow #reactjs #ruby_on_rails #sql #tbls #typescript #visualization #webassembly

Liam ERD is a tool that helps you create easy-to-understand diagrams of your database. It makes interactive diagrams that you can zoom in and out of, filter, and explore easily. This tool is useful for both small and large projects, handling over 100 tables with ease. It's simple to set up and is open-source, meaning you can contribute to it. Using Liam ERD helps you visualize complex database structures quickly, making it easier to understand and work with your data. This saves time and reduces errors compared to drawing diagrams manually.

https://github.com/liam-hq/liam

#go #data_transfer #file_sharing #golang #pake #peer_to_peer #tcp #transfer

Croc is a free and open-source tool that allows you to securely transfer files and folders between any two computers using a simple command line interface. It features end-to-end encryption for safety, supports multiple platforms (Windows, Linux, Mac), and allows for resuming interrupted transfers without needing a central server or port forwarding. You can send files easily by generating a code phrase, making it user-friendly and efficient. The benefit of using Croc is that it simplifies file sharing while ensuring your data remains secure, making it ideal for both personal and professional use.

https://github.com/schollz/croc

#typescript #ai_app_builder #ai_applications #crud_app #crud_application #data_application #data_apps #internal_tools #it_workflows #low_code #low_code_no_code #low_code_platform #no_code #no_code_platform #open_source #rest_api_framework #sql_gui #workflow_apps #workflow_automation #workflow_engine

Budibase is an open-source, low-code platform that helps you build internal tools like forms and apps quickly. It saves engineers a lot of time by making it easy to create and automate business processes. You can connect data from many sources and design your apps with powerful components. Budibase also allows you to automate tasks and integrate with other tools, which helps streamline your work. It's easy to use, even for those without much coding experience, and you can host it yourself or use their cloud service. This makes it a great tool for teams looking to build custom applications efficiently.

https://github.com/Budibase/budibase

#java #automation #data_orchestration #devops #high_availability #infrastructure_as_code #java #low_code #lowcode #orchestration #pipeline #pipeline_as_code #workflow

Kestra is an open-source platform that helps manage complex workflows easily. It uses a simple YAML code to define workflows, which can be automated based on schedules or real-time events. Kestra supports many plugins, allowing integration with various data sources and tools. This makes it easy to automate tasks like data processing and infrastructure management. The platform is scalable, fault-tolerant, and offers real-time monitoring, making it beneficial for teams handling large data pipelines and complex workflows. It simplifies workflow management, reduces errors, and boosts efficiency.

https://github.com/kestra-io/kestra

#typescript #charts #data_grid #data_table #date_picker #date_range_picker #hacktoberfest #react #time_picker

MUI X provides advanced React components like Data Grids, Date Pickers, Charts, and Tree Views for building data-rich apps, offering free MIT-licensed tools for basic needs and paid Pro/Premium plans with advanced features like server-side data handling and AI assistance, saving development time with ready-made solutions while allowing customization for any design system[1][2][4].

https://github.com/mui/mui-x

#typescript #data_layer #local_first #signals #sqlite #state_management #sync_engine

LiveStore is a powerful data layer for apps that uses a reactive SQLite database to manage and sync data instantly across devices, even offline. It replaces traditional state management tools like Redux by allowing you to query and update data reactively with real-time syncing via event-sourcing. It supports many platforms and UI frameworks, offers flexible data modeling, and handles merge conflicts automatically. This means your app can work smoothly offline, sync changes seamlessly, and stay fast and reliable. LiveStore helps you build high-performance, offline-first apps with easy debugging and evolution.

https://github.com/livestorejs/livestore

#typescript #alternative #converter #data_manipulation #developer_tools #devtools #frontend #good_first_issue #image_manipulation #image_processing #javascript #pdf_manipulation #productivity #react #self_hosted #swissarmyknife #tools #typescript #video_manipulation #webapp #website

OmniTools is a self-hosted web app that helps with many tasks like image and video editing, number crunching, and more. It offers tools for resizing images, converting videos, calculating dates, and generating prime numbers. You can run it on your own computer using Docker, which means your data stays local. This app is open-source and free, allowing you to contribute new features or tools easily. Using OmniTools simplifies many everyday tasks and keeps your data private.

https://github.com/iib0011/omni-tools

#other #automl #chatgpt #data_analysis #data_science #data_visualization #data_visualizations #deep_learning #gpt #gpt_3 #jax #keras #machine_learning #ml #nlp #python #pytorch #scikit_learn #tensorflow #transformer

This is a comprehensive, regularly updated list of 920 top open-source Python machine learning libraries, organized into 34 categories like frameworks, data visualization, NLP, image processing, and more. Each project is ranked by quality using GitHub and package manager metrics, helping you find the best tools for your needs. Popular libraries like TensorFlow, PyTorch, scikit-learn, and Hugging Face transformers are included, along with specialized ones for time series, reinforcement learning, and model interpretability. This resource saves you time by guiding you to high-quality, actively maintained libraries for building, optimizing, and deploying machine learning models efficiently.

https://github.com/ml-tooling/best-of-ml-python

#python #data_mining #data_science #deep_learning #deep_reinforcement_learning #genetic_algorithm #machine_learning #machine_learning_from_scratch

This project offers Python code for many basic machine learning models and algorithms built from scratch, focusing on clear, understandable implementations rather than speed or optimization. You can learn how these algorithms work inside by running examples like polynomial regression, convolutional neural networks, clustering, and genetic algorithms. This hands-on approach helps you deeply understand machine learning concepts and build your own custom models. Using Python makes it easier because of its simple, readable code and flexibility, letting you quickly test and modify algorithms. This can improve your skills and confidence in machine learning development.

https://github.com/eriklindernoren/ML-From-Scratch

#html #data_science #education #machine_learning #machine_learning_algorithms #machinelearning #machinelearning_python #microsoft_for_beginners #ml #python #r #scikit_learn #scikit_learn_python

Microsoft’s "Machine Learning for Beginners" is a free, 12-week course with 26 lessons designed to teach classic machine learning using Python and Scikit-learn. It includes quizzes, projects, and assignments to help you learn by doing, with lessons themed around global cultures to keep it engaging. You can access solutions, videos, and even R language versions. The course is beginner-friendly, flexible, and helps build practical skills step-by-step, making it easier to understand and apply machine learning concepts in real-world scenarios. This structured approach boosts your learning retention and prepares you for further study or career growth in ML[1][5].

https://github.com/microsoft/ML-For-Beginners

#typescript #data_visualization #geospatial_analysis #javascript #maps #python #visualization #webgl

deck.gl is a powerful tool that helps you create fast, interactive, and visually impressive maps and data visualizations using WebGL technology. It lets you turn large sets of data into layers like icons, polygons, and text, which you can view in different ways such as maps or 3D scenes. It works well with popular map providers like Google Maps and Mapbox, and supports easy interaction like clicking and filtering. You can use it simply by adding a script or installing it via npm or Python. This makes it easier for you to build custom, high-performance visualizations quickly and with less coding effort.

https://github.com/visgl/deck.gl

#other #artificial_intelligence #artificial_intelligence_projects #awesome #computer_vision #computer_vision_project #data_science #deep_learning #deep_learning_project #machine_learning #machine_learning_projects #nlp #nlp_projects #python

You can access a huge, constantly updated list of over 500 artificial intelligence projects with ready-to-use code covering machine learning, deep learning, computer vision, and natural language processing. This collection includes projects for beginners and advanced users, with links to tutorials, datasets, and real-world applications like chatbots, healthcare, and time series forecasting. Using this resource helps you learn AI by doing practical projects, speeding up your coding skills, and building a strong portfolio for jobs or research. It saves you time searching for quality projects and gives you tested, working code to study and modify.

https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code

#java #cache #caffine #data #draft #fetch #graphql #immer #immutable #immutable_collections #immutable_datastructures #java #jdbc #kotlin #orm #orm_framework #orm_library #orms #redis #redis_cache

Jimmer is a powerful and advanced ORM (Object-Relational Mapping) framework for Java and Kotlin that lets you easily read and write complex data structures without needing to predefine their shapes. It supports dynamic multi-table queries, automatic SQL optimization, and efficient saving of incomplete or nested objects. Jimmer also generates type-safe DTOs (Data Transfer Objects) for complex queries and updates, avoids common problems like "N+1" queries, and offers strong caching and GraphQL support. This means you can build complex business logic faster and with less hassle, improving both development speed and code quality. It works well with modern IDEs and supports both Java and Kotlin seamlessly.

https://github.com/babyfish-ct/jimmer

#rust #artificial_intelligence #big_data #data_engineering #distributed_computing #machine_learning #multimodal #python #rust

Daft is a powerful, easy-to-use data engine that lets you process large-scale data using Python or SQL with high speed and efficiency. It supports complex data types like images and tensors, works well interactively for quick data exploration, and can scale to huge cloud clusters using Ray. Daft integrates smoothly with cloud storage and data catalogs, making it ideal for data engineering, analytics, and machine learning workflows. By using Daft, you can handle big, multimodal datasets faster and more flexibly, improving your ability to analyze and prepare data for AI models without complex setup or slowdowns.

https://github.com/Eventual-Inc/Daft

#go #archival #data_archiving #data_import #family_history #self_hosted #timeline

Timelinize helps you organize your personal data from different sources like photos, messages, and social media into a single timeline on your computer. This keeps your data private and under your control, unlike cloud services. You can import data from many places, view it on a map, and see conversations across different platforms. It's like having a personal journal that you can add to and keep forever, without relying on companies to store it for you. This way, you can keep your memories safe and easily look back at them whenever you want.

https://github.com/timelinize/timelinize