Search Results for "deep" - Page 9
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Showing 1152 open source projects for "deep"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
...Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. By lowering the hardware requirements needed to work with large models, bitsandbytes helps make modern AI development more accessible to researchers and engineers. ... -
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NVIDIA Earth2Studio
Open-source deep-learning framework
...The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. Users can extend Earth2Studio with optional model packs, advanced data interfaces, statistical operators, and backend integrations that support flexible workflows from simple tests to large-scale operational inference. -
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Rust Course
It has been the world's most popular language for 8 consecutive years
...The course is carefully designed with a structured catalog, vivid and approachable language, and an engaging style that avoids the dry and mechanical tone of many technical books. It covers the basics of Rust, such as ownership, borrowing, lifetimes, traits, and generics, but also dives deep into advanced topics like performance optimization, linked list implementations, async programming with Tokio, standard library internals, Cargo usage, and WebAssembly development. The project emphasizes practical learning through exercises, helping users approach Rust study as if it were a university course. It also provides a "Cookbook" section of practical code snippets for common tasks such as file operations, regex handling, and database interactions, allowing learners to quickly reference solutions without searching externally. -
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Vuls
Agentless vulnerability scanner for Linux/FreeBSD
...If you don't want the central Vuls server to connect to each server by SSH, you can use Vuls in the Local Scan mode. Fast scan mode scans without root privilege, no internet access, almost no load on the scan target server. Deep scan mode scans in more detail. It is possible to acquire the state of the server executing some commands. Vuls v0.5.0 warns not-restarting-processes which updated before but not restarting yet. And detect processes affecting software update in advance. -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
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Roo Code
Roo Code gives you a whole dev team of AI agents in your code editor
Roo Code is an AI-powered software engineering platform that works interactively in your IDE and autonomously in the cloud to help teams ship faster. It combines a powerful VS Code extension with cloud-based agents that can take on real development tasks across GitHub, Slack, and the web. Designed to work on your terms, Roo Code gives you full control locally while enabling delegation and parallel execution at scale. Its model-agnostic architecture ensures flexibility as AI models and... -
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Decky Loader
A plugin loader for the Steam Deck
Decky Loader is a homebrew plugin launcher built for the Steam Deck that enables users to extend and customize the console’s functionality through a dynamic plugin ecosystem integrated directly into the SteamOS interface. It acts as a middleware layer that injects and manages plugins, allowing deep customization such as modifying UI elements, changing system sounds, adjusting display properties, and extending system behavior beyond stock capabilities. The platform is designed to persist across system updates and reboots, ensuring continuity of installed plugins without manual reconfiguration. It supports advanced developer features such as two-way communication between plugins and the loader, as well as interoperability between Python backends and TypeScript React frontends. ... -
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Ellama
Ellama is a tool for interacting with large language models from Emacs
Ellama is an Emacs integration that brings large language model capabilities directly into the Emacs editor environment, focusing on extensibility and deep workflow integration. It allows users to interact with local or remote models through a unified interface embedded within their editing sessions. The tool is designed to align with Emacs’ philosophy of customization, enabling users to define prompts, commands, and behaviors tailored to their workflows. It supports conversational interactions, code assistance, and text transformation tasks, making it useful for both programming and writing scenarios. ... -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. ... -
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
SimpleHTR is an open-source implementation of a handwriting text recognition system based on deep learning techniques. The project focuses on converting images of handwritten text into machine-readable digital text using neural networks. The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting. It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. ... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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NVIDIA PhysicsNeMo
Open-source deep-learning framework for building and training
NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building artificial intelligence models that incorporate physical laws and scientific knowledge into machine learning workflows. The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. -
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Reco-papers
Classic papers and resources on recommendation
Reco-papers is a curated repository that collects influential research papers, technical resources, and industry materials related to recommender systems and recommendation algorithms. The project organizes a large body of literature into thematic sections such as classic recommender systems, exploration-exploitation strategies, deep learning–based recommendation models, and cold-start mitigation techniques. It serves as a reference library for researchers and engineers who want to explore foundational and cutting-edge work in recommendation technologies. The repository includes papers from academic institutions and industry organizations and groups them according to topics such as retrieval and reranking, reinforcement learning in recommendation, and feature engineering infrastructure. ... -
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Data-Science-Interview-Questions-Answers
Curated list of data science interview questions and answers
...The repository focuses on core data science fundamentals rather than acting as a software framework, which makes it especially useful as a study and revision resource. Its content is organized into subject-specific documents that cover machine learning, deep learning, statistics, probability, Python, SQL and databases, and resume-based interview questions. That structure makes it practical for users who want to study by topic, strengthen weak areas, or simulate the range of questions they may encounter in interviews. -
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Telos
Telos is an open-sourced framework for creating Deep Context
...It encourages capturing a “context file” that outlines mission, goals, problems, strategies, KPIs, and other core components of meaningful work. The project emphasises creating deep context about matters that humans care about, making underlying intentions explicit rather than implicit. It is structured so that you can document your purpose, link it to your strategies and metrics, and thereby maintain clarity and alignment over time. Although lightweight in implementation (for example a .md context file), its ambition is philosophical: helping actors live intentionally and track progress toward purpose. ... -
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Magika
Fast and accurate AI powered file content types detection
Magika is an AI-powered file-type detector that uses a compact deep-learning model to classify binary and textual files with high accuracy and very low latency. The model is engineered to be only a few megabytes and to run quickly even on CPU-only systems, making it practical for desktop apps, servers, and security pipelines. Magika ships as a command-line tool and a library, providing drop-in detection that improves on traditional “magic number” and heuristic approaches, especially for ambiguous or short files. ... -
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Sysdig
Linux system exploration and troubleshooting tool
...Unify threat detection and incident response across containers, Kubernetes, and cloud with out-of-the-box Falco rules leveraging syscalls, Kubernetes audit logs and cloud logs. Gain deep insight with container and Kubernetes monitoring that is fully Prometheus compatible. Validate compliance against standards like PCI, NIST and SOC2 for containers, hosts, Kubernetes and cloud. Sysdig created Falco, the open standard for runtime threat detection for containers, hosts, Kubernetes and cloud. -
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r2modman
A simple and easy to use mod manager for several games
...Built with Electron, TypeScript, and Vue, the application focuses on providing a clean, beginner-friendly interface so players can manage complex mod setups without deep technical knowledge. It allows users to download, enable, disable, update, and uninstall mods with a few clicks while keeping the base game playable through normal launchers like Steam. The tool supports multiple popular mod-heavy games such as Risk of Rain 2, Valheim, GTFO, and others, making it a central hub for Thunderstore-based modding workflows. ... -
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Zydis
Fast and lightweight x86/x86-64 disassembler and code generation lib
...It focuses on performance and accuracy, making it suitable for applications such as reverse engineering, debugging, and binary analysis. The library provides detailed information about decoded instructions, including operands, prefixes, and instruction lengths, enabling deep inspection of executable code. It is designed to be easy to integrate, with a clean and minimal API that reduces complexity for developers. Zydis also emphasizes portability, allowing it to run across multiple platforms and environments. Its efficiency makes it particularly useful in scenarios where large volumes of code need to be analyzed quickly. ... -
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MiniCPM4.1
Achieving 3+ generation speedup on reasoning tasks
MiniCPM4.1 is an enhanced iteration of the MiniCPM4 architecture, introducing improvements in reasoning capabilities, inference speed, and hybrid operation modes that allow dynamic switching between deep reasoning and standard generation. It builds upon the same efficiency-focused philosophy but further optimizes decoding performance, achieving substantial speed gains in reasoning-intensive tasks while maintaining high-quality outputs. One of its key innovations is the hybrid reasoning mode, which allows developers to control whether the model engages in deeper reasoning processes or faster responses depending on the use case. ... -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
...The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis. The repository integrates numerous popular machine learning frameworks and libraries such as scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. It aims to strike a balance between theoretical explanation and practical coding by demonstrating algorithms both from scratch and using established libraries. ... -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. The repository supports multiple neural network backbones such as ResNet, DenseNet, MobileNet, and ShuffleNet, enabling experimentation with different architectures depending on performance requirements. ... -
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DeepSearcher
Open Source Deep Research Alternative to Reason and Search
DeepSearcher is an open-source “deep research” style system that combines retrieval with evaluation and reasoning to answer complex questions using private or enterprise data. It is designed around the idea that high-quality answers require more than top-k retrieval, so it orchestrates multi-step search, evidence collection, and synthesis into a comprehensive response. -
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Underscore.js
JavaScript's utility belt
...Instead of extending built-in objects or modifying prototypes, Underscore provides its helpers in a single _ namespace, enabling cross-browser support and consistent behaviour across environments. It offers map/filter/reduce, deep-cloning, templating, object traversal, function binding, and more, which makes it easier to write concise, expressive code rather than verbose loops and conditional logic. Many other libraries and frameworks (including Backbone) historically depended on or partnered with Underscore for their utility functions. Even though ES6+ and Lodash have overshadowed some of its role, Underscore remains influential and educational for how to design utility APIs. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). -
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ReinforcementLearning.jl
A reinforcement learning package for Julia
...Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Make it easy for new users to run benchmark experiments, compare different algorithms, and evaluate and diagnose agents. Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms. Provide elaborately designed components and interfaces to help users implement new algorithms. ... -
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AlphaFold 3
AlphaFold 3 inference pipeline
AlphaFold 3, developed by Google DeepMind, is an advanced deep learning system for predicting biomolecular structures and interactions with exceptional accuracy. This repository provides the complete inference pipeline for running AlphaFold 3, though access to the model parameters is restricted and must be obtained directly from Google under specific terms of use. The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. ...
