Search Results for "learning" - Page 72
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Showing 4736 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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AgentGPT
🤖 Assemble, configure & deploy autonomous AI Agents in your browser
.... 🤖 AgentGPT allows you to configure and deploy Autonomous AI agents. Name your own custom AI and have it embark on any goal imaginable. It will attempt to reach the goal by thinking of tasks to do, executing them, and learning from the results 🚀. By sponsoring this free, open-source project, you not only have the opportunity to have your avatar/logo featured below, but also get the exclusive chance to chat with the founders!🗣️ 👉 Click here to support the project: https://github.com/sponsors/reworkd-admin -
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Liveliness and Face Identification
Leading free and open-source liveliness check &face recognition system
This is a free and open-source liveliness check and face recognition project. Essentially, it is an application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use. The application is customizable react based mobile friendly UI and Python based backend. The program is a real-time face detection application. It allows you to detect faces using your webcam and displays the video feed with oval drawn around the detected faces. When you run the program, a GUI window will appear. ... -
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CoTracker
CoTracker is a model for tracking any point (pixel) on a video
CoTracker is a learning-based point tracking system that jointly follows many user-specified points across a video, rather than tracking each point independently. By reasoning about all tracks together, it can maintain temporal consistency, handle mutual occlusions, and reduce identity swaps when trajectories cross. The model takes sparse point queries on one frame and predicts their sub-pixel locations and a visibility score for every subsequent frame, producing long, coherent trajectories. ... -
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React Demos
A collection of simple demos of React.js
...Many examples are deliberately minimal, showing just enough HTML, CSS, and JavaScript to highlight the React pattern being taught. The structure encourages hands-on learning: open a demo, modify a prop or state update, and observe how the UI reacts. It works well as a study companion for beginners or as a quick refresher before building larger applications. -
$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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javascript-algorithms
JavaScript implementations of computer science algorithms
javascript-algorithms is a repository that implements a comprehensive collection of classic data structures and algorithmic solutions in JavaScript — including sorting, searching, graph algorithms, dynamic programming, and more. It serves as a learning and reference resource for developers wanting to understand how standard algorithms can be expressed in JavaScript, bridging the gap between theoretical algorithm knowledge and practical implementation usable in web or Node.js contexts. Because algorithms are implemented in a language (JavaScript) many developers use daily, the repo becomes especially useful for students, interview-preparation candidates, or anyone wanting to learn algorithmic thinking without switching languages. ... -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a... -
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Autolabel
Label, clean and enrich text datasets with LLMs
Autolabel is a Python library to label, clean and enrich datasets with Large Language Models (LLMs). Autolabel data for NLP tasks such as classification, question-answering and named entity recognition, entity matching and more. Seamlessly use commercial and open-source LLMs from providers such as OpenAI, Anthropic, HuggingFace, Google and more. -
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ReplitLM
Inference code and configs for the ReplitLM model family
...The models were trained using modern deep learning techniques and large-scale GPU infrastructure to achieve strong performance in code completion and generation tasks. -
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
...DLWorkspace (aka. DLTS) AML (Azure Machine Learning) and other cloud options. NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. -
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FastAI.jl
Repository of best practices for deep learning in Julia
FastAI.jl is a Julia library for training state-of-the-art deep learning models. From loading datasets and creating data preprocessing pipelines to training, FastAI.jl takes the boilerplate out of deep learning projects. It equips you with reusable components for every part of your project while remaining customizable at every layer. FastAI.jl comes with support for common computer vision and tabular data learning tasks, with more to come. -
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YoloV3 Implemented in TensorFlow 2.0
YoloV3 Implemented in Tensorflow 2.0
YoloV3 Implemented in TensorFlow 2.0 is built using TensorFlow 2.0. The project provides a modern deep learning implementation of the popular YOLOv3 algorithm, which is widely used for real-time object detection in images and video streams. YOLOv3 works by dividing an image into grid regions and predicting bounding boxes and class probabilities simultaneously, allowing objects to be detected quickly and efficiently. The repository includes training scripts, inference tools, and configuration files that make it possible to train custom object detection models on user-defined datasets. ... -
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SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion
SoftVC VITS Singing Voice Conversion is a deep learning project focused on singing voice conversion, allowing users to transform one voice into another while preserving melody and timing. Unlike traditional text-to-speech systems, it specializes specifically in singing scenarios and does not provide general TTS functionality. The project leverages neural network architectures derived from VITS and SoftVC research to achieve high-quality voice transformation. -
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ICCV2023-Paper-Code-Interpretation
ICCV2021/2019/2017 Paper/Code/Interpretation/Live Broadcast Collection
ICCV2023-Paper-Code-Interpretation is a curated repository that provides explanations and interpretations of code associated with research papers presented at the International Conference on Computer Vision (ICCV) 2023. The project focuses on helping researchers and students better understand how complex computer vision algorithms described in academic papers are implemented in practice. Many state-of-the-art research papers provide only limited implementation details, which can make... -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be... -
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LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
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ParamOS
A Cosmos-based Operating System (Developed for Learning Purpose)
ParamOS is a basic operating system made using Cosmos OS Kit in Visual Studio (C#/.NET). The command line OS has five commands: ‘namaste’ for greetings, ‘about’ to learn more about ParamOS, 'gui' to load the Graphical User Interface (GUI), ‘restart’ to reboot the OS and ‘shutdown’ to shut down the computer. -
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KongFuOfArchitect
Architect's Kung Fu tutorial collection Article collection
KongFuOfArchitect is a curated collection of tutorials and articles aimed at software architects. It encompasses a wide range of topics, including programming paradigms, microservices, essential algorithms, and security practices, serving as a comprehensive resource for architectural knowledge. -
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D2L.ai
Interactive deep learning book with multi-framework code
...Offers sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist. -
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fe4ml-zh
Feature Engineering for Machine Learning
fe4ml-zh is an open-source project that provides a Chinese translation and structured documentation of the book Feature Engineering for Machine Learning. The repository aims to make advanced feature engineering concepts accessible to a broader audience by translating the content and organizing it into readable documentation and code examples. Feature engineering is a critical component of machine learning pipelines because it determines how raw data is transformed into features that algorithms can use effectively. ... -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. ... -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. ... -
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Llama 2 Everywhere (L2E)
Llama 2 Everywhere (L2E)
...The project focuses on simplicity and educational clarity by implementing inference for LLaMA-style models in a compact C program rather than relying on large machine learning frameworks. Developers can train models using a Python training pipeline and then run inference using a lightweight C implementation that requires very few dependencies. The architecture mirrors the structure of the LLaMA-2 model family, allowing compatible model checkpoints to be converted and executed within the simplified runtime environment. ...