tensorflowmodel-card-toolkitArchived
452 stele88 fork-uriPythonApache-2.05 vizualizăriwww.tensorflow.org/responsible_ai/model_card_toolkit/guide
The Model Card Toolkit (MCT) streamlines and automates generation of Model Cards [1], machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with…
Principalele funcționalități ale tensorflow/model-card-toolkit sunt: AI Security and Red Teaming, Instrumente pentru dezvoltatori.
Alternativele open-source pentru tensorflow/model-card-toolkit includ: lutzroeder/netron — Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders… trigaten/learn_prompting — Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques… 0xax/erlang-bookmarks — All about erlang programming language [powerd by community]. 0xradi/ai-ml-security-reading-list. 21st-dev/magic-mcp — Magic MCP is a Model Context Protocol server and AI component generator that translates natural language descriptions… 0xerwin1/dbflux — A fast, keyboard-first database client built with Rust and GPUI.
Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users
Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th
All about erlang programming language powerd by community
