3 repositorios
Awesome GitHub RepositoriesJavaScript Model Training
The process of defining, training, and optimizing neural networks using JavaScript.
Distinct from Model Training Optimizers: Candidates focus on vision models or general optimizers; this describes the broad capability of training models in JS.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · JavaScript Model Training. Refine with filters or upvote what's useful.
Awesome JavaScript Model Training GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- tensorflow/tfjs
tensorflow/tfjs
19,134Ver en GitHubTensorFlow.js is a JavaScript machine learning library used for training and deploying models in web browsers and server-side environments. It functions as a browser-based model trainer, a WebAssembly inference engine, and a WebGPU accelerated tensor library for low-level linear algebra. The project also includes a model converter to transform Python-based models into optimized formats for JavaScript execution. The library distinguishes itself through a pluggable backend architecture that allows mathematical operations to be executed via CPU, WebGL, or WebGPU. It supports the conversion of Py
Provides the capability to define, train, and optimize neural networks using JavaScript.
TypeScript - tensorflow/tfjs-core
tensorflow/tfjs-core
8,437Ver en GitHubTensorFlow.js is a JavaScript machine learning library and browser-based runtime used to build, train, and execute models. It functions as a WebGL accelerated tensor engine, providing a foundation for high-performance linear algebra operations and an automatic differentiation framework for computing gradients. The project distinguishes itself through its ability to run machine learning directly in web environments, supporting both client-side inference and browser-based training. It enables the deployment of Python-based models by converting Keras or TensorFlow models into compatible formats
Provides a full environment for developing and optimizing neural networks using JS and automatic differentiation.
TypeScriptdeep-learningdeep-neural-networksgpu-acceleration - snowkylin/tensorflow-handbook
snowkylin/tensorflow-handbook
3,927Ver en GitHubEste proyecto es un recurso educativo integral y un manual de tutoriales para construir, entrenar y desplegar modelos de machine learning usando TensorFlow 2. Sirve como una guía de aprendizaje estructurada que cubre conceptos fundamentales de deep learning, incluyendo arquitecturas de redes neuronales, diferenciación automática y operaciones con tensores. El manual proporciona orientación técnica sobre cómo optimizar la eficiencia de ejecución mediante la gestión de memoria de GPU, entrenamiento distribuido y cuantización de modelos. También incluye guías detalladas para construir pipelines de datos de alto rendimiento y exportar modelos para servidores de producción, dispositivos móviles y navegadores web. El material abarca una amplia gama de capacidades, incluyendo el desarrollo de modelos con redes convolucionales y recurrentes, la implementación de funciones de pérdida y capas personalizadas, y el uso de modelos preentrenados para transfer learning. También aborda estrategias de despliegue para dispositivos edge y el uso de entornos de ejecución en la nube para aceleración por hardware. El recurso está implementado como una colección de Jupyter Notebooks.
Enables the definition and training of neural networks directly within JavaScript environments.
Jupyter Notebook
