All current Interpretability Courses courses in 2024
7 Courses
Responsible AI for Developers: Interpretability & Transparency - Polski
Responsible AI for Developers: Interpretability & Transparency - Polski
Na tym szkoleniu przedstawiamy koncepcje interpretowalności i przejrzystości AI. Omawiamy na nim, jak ważna jest przejrzystość AI dla deweloperów i inżynierów. Pokazujemy praktyczne techniki i narzędzia, które pomagają osiągnąć interpretowalność oraz przejrzystość zarówno w.
Responsible AI for Developers: Interpretability & Transparency - 繁體中文
Responsible AI for Developers: Interpretability & Transparency - 繁體中文 | Coursera
本課程旨在說明 AI 的可解釋性和透明度概念、探討 AI 透明度對開發人員和工程師的重要性。課程中也會介紹實務方法和工具，有助於讓資料和 AI 模型透明且可解釋。
University: Provider: Coursera
Categories:
Artificial Intelligence Courses,
Machine Learning Courses,
Data S.
Responsible AI for Developers: Interpretability & Transparency - Italiano
Responsible AI for Developers: Interpretability & Transparency - Italiano
Questo corso introduce i concetti di interpretabilità e la trasparenza dell'AI. Parla dell'importanza della trasparenza dell'AI per sviluppatori ed engineer. Illustra metodi e strumenti pratici per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei.
Responsible AI for Developers: Interpretability & Transparency
Responsible AI for Developers: Interpretability & Transparency
This course, Responsible AI for Developers: Interpretability & Transparency, introduces key concepts of AI interpretability and transparency, emphasizing their importance for developers and engineers. Provided by Pluralsight, it explores practical methods and tools to help achieve in.
Developing Explainable AI (XAI)
Developing Explainable AI (XAI)
As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy.
This course pro.
AWS ML Engineer Associate 2.1 Choose a Modeling Approach (Korean)
AWS ML 스택 계층을 살펴보고 AWS 서비스를 사용하여 일반적인 비즈니스 문제를 해결하는 방법을 알아봅니다. 이 과정에서는 기계 학습 태스크에 Amazon SageMaker를 사용하는 방법과 적절한 모델을 선택하기 위한 전략을 검토하는 방법을 살펴봅니다.
또한 사전 훈련된 Amazon SageMaker JumpStart ML 솔루션의 특정 시나리오를 중점적으로 설명하고 비즈니스 요구 사항에.
