Responsible AI for Developers: Interpretability & Transparency
|
This course introduces concepts of AI interpretability and transparency.
This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.
User Reviews
Be the first to review “Responsible AI for Developers: Interpretability & Transparency”
Related Products
Building Generative Adversarial Networks
Learn to design and implement GANs for generating realistic data samples in various applications.
Advanced Computer Vision and Deep Learning
Explore advanced topics in computer vision and deep learning techniques for complex image analysis tasks.
Computer Vision
Study techniques and applications in computer vision for processing and interpreting visual data.
Digital Arts and AI Tools Fluency
Gain proficiency in using AI tools for digital art creation, enhancing creativity and workflow.
Introduction to Machine Learning with TensorFlow
Explore machine learning concepts and applications using the TensorFlow framework.
AI for Healthcare
Understand the applications of AI in the healthcare sector, including diagnosis, treatment, and patient management.
There are no reviews yet.