Search Results for "learning" - Page 6
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Showing 1543 open source projects for "learning"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Network Discovery Software | JDisc DiscoveryJDisc Discovery is a comprehensive network inventory and IT asset management solution designed to help organizations gain clear, up-to-date visibility into their IT environment. It automatically scans and maps devices across the network, including servers, workstations, virtual machines, and network hardware, to create a detailed inventory of all connected assets. This includes critical information such as hardware configurations, software installations, patch levels, and relationshipots between devices.
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
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MetaClaw
Just talk to your agent
MetaClaw is an AI or agent-oriented system that appears to focus on advanced control, coordination, or training of autonomous agents, potentially within reinforcement learning or tool-using environments. The project likely emphasizes meta-level reasoning, where agents are not only executing tasks but also adapting their strategies based on feedback and performance signals. It may incorporate mechanisms for learning from interactions, improving decision-making over time, and generalizing across different domains. ... -
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X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
...The tool is built with an interactive graphical interface that simplifies annotation workflows and allows users to draw and edit labels directly on visual data. It also supports a wide range of export formats compatible with popular machine learning pipelines, making it easier to integrate with training frameworks. -
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DINOv3
Reference PyTorch implementation and models for DINOv3
DINOv3 is the third-generation iteration of Meta’s self-supervised visual representation learning framework, building upon the ideas from DINO and DINOv2. It continues the paradigm of learning strong image representations without labels using teacher–student distillation, but introduces a simplified and more scalable training recipe that performs well across datasets and architectures. DINOv3 removes the need for complex augmentations or momentum encoders, streamlining the pipeline while maintaining or improving feature quality. ... -
Network Performance Monitoring | StatseekerUsing just a single server or virtual machine, Statseeker can be up and running within minutes, and discovering your entire network in less than an hour, without any significant effect on your bandwidth availability.
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Rasa
Open source machine learning framework to automate text conversations
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. -
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WiFi DensePose
Turn WiFi signals into real-time human pose estimation and detection
...The project demonstrates how commodity mesh routers and signal processing techniques can be leveraged to infer dense human pose information, even through obstacles such as walls. It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. The repository includes components for data processing, model inference, and real-time visualization, making it suitable for research and experimental deployments. Its architecture emphasizes performance and reproducibility, allowing developers to explore non-visual motion capture systems using accessible hardware. ... -
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FLEXible
Federated Learning (FL) experiment simulation in Python
FLEXible (Federated Learning Experiments) is a Python framework offering tools to simulate FL with deep learning. It includes built-in datasets (MNIST, CIFAR10, Shakespeare), supports TensorFlow/PyTorch, and has extensions for adversarial attacks, anomaly detection, and decision trees. -
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DocTR
Library for OCR-related tasks powered by Deep Learning
DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document... -
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PyTorch-Tutorial-2nd
CV, NLP, LLM project applications, and advanced engineering deployment
PyTorch-Tutorial-2nd is an open-source educational repository that provides structured tutorials for learning deep learning with the PyTorch framework. The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization strategies. ... -
GoAnywhere Managed File Transfer (MFT)GoAnywhere MFT provides secure managed file transfer for enterprises. Deployable on-premise, in the cloud, or in hybrid environments, GoAnywhere MFT software enables organizations to exchange data among employees, customers, and trading partners, as well as between systems, securely. GoAnywhere MFT was a recipient of the Cybersecurity Excellence Award for Secure File Transfer.
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MLC LLM
Universal LLM Deployment Engine with ML Compilation
MLC LLM is a machine learning compiler and deployment framework designed to enable efficient execution of large language models across a wide range of hardware platforms. The project focuses on compiling models into optimized runtimes that can run natively on devices such as GPUs, mobile processors, browsers, and edge hardware. By leveraging machine learning compilation techniques, mlc-llm produces high-performance inference engines that maintain consistent APIs across platforms. ... -
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PythonPark
Python open source project "The Road to Self-Study Programming"
PythonPark is a large, curated “learning playground” for Python — essentially a comprehensive self-study meta-repository aimed at helping learners progress in Python programming, data science, machine learning, web scraping, and software engineering practices. It aggregates tutorials, learning guides, project examples, and resources across topics: from Python basics and data structures to machine learning, web scraping, and even interview preparation and “programmer life” guidance. ... -
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FlashAttention
Fast and memory-efficient exact attention
FlashAttention is a high-performance deep learning optimization library that reimplements the attention mechanism used in transformer models to be significantly faster and more memory-efficient than standard implementations. It achieves this by using IO-aware algorithms that minimize memory reads and writes, reducing the quadratic memory overhead typically associated with attention operations. -
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Book4_Power-of-Matrix
Book_4_Matrix Power | The Iris Book: From Addition, Subtraction
...The repository is continuously updated and intended to accompany the broader Visualize-ML learning ecosystem. Overall, it serves as a visually driven mathematical foundation for students preparing for data science and machine learning work. -
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Adversarial Robustness Toolbox
Adversarial Robustness Toolbox (ART) - Python Library for ML security
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, sci-kit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, generation, certification, etc.). -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
...The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting. -
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RL Games
RL implementations
rl_games is a high-performance reinforcement learning framework optimized for GPU-based training, particularly in environments like robotics and continuous control tasks. It supports advanced algorithms and is built with PyTorch. -
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. Distributed training without a master node: Distributed Hash Table allows connecting computers in a decentralized network. Fault-tolerant backpropagation: forward and backward passes succeed even if some nodes are unresponsive or take too long to respond. -
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DeepVariant
DeepVariant is an analysis pipeline that uses a deep neural networks
DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file. DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio extends DeepVariant's functionality, allowing it to utilize the power of neural networks to predict genomic variants in trios or duos. ... -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
...It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. ... -
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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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Jina-Serve
Build multimodal AI applications with cloud-native stack
...Jina Serve focuses on making it easier to turn machine learning models into production-ready services without forcing developers to manage complex infrastructure manually. The framework supports many major machine learning libraries and data types, making it suitable for multimodal AI systems that process text, images, audio, and other inputs. -
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Appfl
Advanced Privacy-Preserving Federated Learning framework
APPFL (Advanced Privacy-Preserving Federated Learning) is a Python framework enabling researchers to easily build and benchmark privacy-aware federated learning solutions. It supports flexible algorithm development, differential privacy, secure communications, and runs efficiently on HPC and multi-GPU setups. -
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Substra
Low-level Python library used to interact with a Substra network
An open-source framework supporting privacy-preserving, traceable federated learning and machine learning orchestration. Offers a Python SDK, high-level FL library (SubstraFL), and web UI to define datasets, models, tasks, and orchestrate secure, auditable collaborations. -
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Mlxtend
A library of extension and helper modules for Python's data analysis
Mlxtend (machine learning extensions) is a Python library of useful tools for day-to-day data science tasks.
