Search Results for "learning" - Page 77
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Showing 4819 open source projects for "learning"
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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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python-projects
Unveil captivating games, elegant GUI apps, and potent utilities
"Join the magic of 'Python-Projects' by MRayanAsim: Unveil captivating games, elegant GUI apps, and potent utilities with a touch of machine learning enchantment. Awaken Python's full potential and spread the magic by starring the repository today!" 🚀🐍✨ -
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Bonzomatic
Live shader coding tool and Shader Showdown workhorse
...It provides a platform for writing and visualizing shaders in real-time, enabling artists and developers to create captivating visual effects on the fly. Bonzomatic supports various shading languages and platforms, making it a versatile tool for both learning and showcasing shader programming skills. -
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Guia Extenso de Programação
An extensive guide of information with a vast content
...The repository also includes translation options and encourages community contributions through pull requests and issues. Overall, it functions as a large reference hub for self-directed technology learning. -
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DataGym.ai
Open source annotation and labeling tool for image and video assets
DATAGYM enables data scientists and machine learning experts to label images up to 10x faster. AI-assisted annotation tools reduce manual labeling effort, give you more time to finetune ML models and speed up your go to market of new products. Accelerate your computer vision projects by cutting down data preparation time up to 50%. A machine learning model is only as good as its training data. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Lumi-HSP
This is an AI language model that can predict Heart failure or stroke
Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone. -
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The Fish Cookbook
From Shell to Plate: Savor the Zest of Fish
The Fish Cookbook is a curated collection of practical recipes, tutorials, and user-contributed examples for the Fish shell, designed to help both beginners and intermediate users get the most out of Fish’s powerful and expressive scripting environment. Instead of treating Fish as a niche or simple alternative shell, this cookbook showcases real solutions for common shell tasks such as prompt customization, tab completion, scripting patterns, task automation, environment configuration, and... -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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aqueduct LLM
Aqueduct allows you to run LLM and ML workloads on any infrastructure
Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure. Aqueduct is an open-source MLOps framework that allows you to write code in vanilla Python, run that code on any cloud infrastructure you'd like to use, and gain visibility into the execution and performance of your models and predictions. Aqueduct's Python native API allows you to define ML tasks in regular Python code. -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
...This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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MOOS
C# x64 operating system programming with the .NET native
...Written in Rust, MOOS provides a safe and modern platform to explore low-level system design, with a minimal but functional kernel that supports multitasking, a virtual memory manager, and a tiny standard library. It targets x86_64 and runs on QEMU, making it suitable for students and developers learning about OS fundamentals or testing novel ideas in kernel development. -
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Learning Prompt
Free prompt engineering online course
Learning-Prompt is a curated repository of prompts, techniques, and instructional patterns designed to help people construct better prompts for AI systems, including generation, reasoning, and debugging tasks. It collects examples of effective prompt formats, chains of thought, strategy scaffolds, and comparative methods that help yield consistent, high-quality results. -
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TF2DeepFloorplan
TF2 Deep FloorPlan Recognition using a Multi-task Network
TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. This repo contains a basic procedure to train and deploy the DNN model suggested by the paper 'Deep Floor Plan Recognition using a Multi-task Network with Room-boundary-Guided Attention'. It rewrites the original codes from zlzeng/DeepFloorplan into newer versions of Tensorflow and Python. -
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DevOps Resources
DevOps resources
This repository aggregates useful resources, references, roadmaps, and learning materials in various areas of DevOps including Linux, Jenkins, AWS, SRE, Prometheus, Docker, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL/NoSQL, Azure, GCP, and more. Curated tooling and technology references across multiple DevOps domains. Roadmaps and learning paths for building DevOps competence. It is meant as a guide for learners and practitioners to discover tools, concepts, and tutorials relevant to DevOps roles. ... -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
...Trial: A description of a multi-step machine learning workflow. Each step in the workflow is described by a Trial Component. There is no relationship between Trial Components such as ordering. Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc. -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and... -
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gpustat
A simple command-line utility for querying and monitoring GPU status
...The utility retrieves data through NVIDIA’s NVML bindings and displays information such as temperature, utilization, memory usage, and running processes directly in the terminal. Because it is easy to install via pip and requires minimal configuration, gpustat is widely used in machine learning environments, research clusters, and shared GPU servers. The tool also supports watch mode for continuous monitoring and JSON output for integration into automation pipelines. Overall, gpustat focuses on speed, clarity, and scriptability, making it especially useful for engineers who need quick GPU visibility without heavy monitoring stacks. -
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The HaskellR project
The full power of R in Haskell
...HaskellR allows Haskell functions to seamlessly call R functions and vice versa. It provides the Haskell programmer with the full breadth of existing R libraries and extensions for numerical computation, statistical analysis and machine learning. Optionally, pass in the --nix flag to all commands if you have the Nix package manager installed. Nix can populate a local build environment including all necessary system dependencies without touching your global filesystem. Use it as a cross-platform alternative to Docker. -
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TKEStack
Native Kubernetes container management platform
...Provides an intuitive UI interface to support visualization and YAML import and other resource creation and editing methods, enabling users to run containers without learning all Kubernetes concepts up-front. An abstract project-level resource container that supports multiple namespace management and deployment applications across multiple clusters. Unified authorization management, not only at the cluster management level but even at the Kubernetes resource level. Integration with your existing authentication mechanisms, including LDAP, OIDC, front proxy, and public OAuth providers such as GitHub. -
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Paddle Quantum
Paddle Quantum
...It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for the scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts. -
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Coframe
Coframe brings your UX to life with AI-powered optimization
...Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your users. Resulting performance data is fed back in to continuously improve your platform's content. With Coframe, your website or app works for you 24/7, not the other way around. All it takes to get up and running is a few lines of code. ... -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
...Each chapter is a standalone Jupyter notebook, with runnable code, explanatory prose, visuals, and examples showing how to handle data-wrangling, exploratory data analysis, machine learning workflows, and visualization. The repository is freely available and the code is released under the MIT license; the textual content is released under a Creative Commons license. Users can also launch the notebooks in Google Colab or Binder directly, making it extremely accessible. -
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DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. ... -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack