2 Repos
Awesome GitHub RepositoriesCustom ML Components
Extensibility mechanisms for defining custom neural network layers, models, and metrics using high-level language source code.
Distinct from Custom Python Components: Distinct from Custom Python Components: specifically targets ML-specific building blocks like layers and metrics rather than general system logic.
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Awesome Custom ML Components GitHub Repositories
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- fchollet/keras
fchollet/keras
64,095Auf GitHub ansehenKeras is a high-level deep learning API used to design, build, and train neural networks for tasks such as computer vision, natural language processing, and time series forecasting. It provides a framework for defining model architectures and optimizing weights through a structured interface. The project is defined by a backend-agnostic design that allows the same model code to run across different compute engines. This multi-backend execution enables users to swap underlying engines to optimize for specific hardware or performance requirements. The system supports distributed model training
Enables the creation of custom layers, models, and metrics that remain compatible across different compute engines.
Python - modelscope/modelscope
modelscope/modelscope
8,718Auf GitHub ansehenModelScope is a comprehensive machine learning platform that functions as a model hub, training framework, inference engine, and cloud development environment. It provides a centralized repository for discovering, downloading, and managing pre-trained models and datasets across multiple modalities, including natural language, vision, and speech. The platform features a unified interface for multimodal model inference and a standardized framework for fine-tuning and evaluating large-scale models. It supports distributed training to scale workloads across multiple processors and provides contai
Allows the modification of specific pipeline modules to implement custom logic during model inference and training.
Pythoncvdeep-learningmachine-learning
