Introduction to Reliable Deep Learning
Introduction to Reliable Deep Learning
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This course introduces you to the world of reliable deep learning, a critical discipline focused on developing machine learning models that not only make accurate predictions but also understand and communicate their own uncertainty. You'll learn how to create AI systems that are trustworthy, robust, and adaptable, particularly in high-stakes scenarios where errors can have significant consequences.
Course Info
Objectives
- Define reliable deep learning's core traits and contrast its goals and methods with traditional deep learning.
- Examine real-world use cases for reliable deep learning, highlighting the risks of overconfident AI predictions.
- Compare and contrast ensemble methods and SNGP as techniques for improving model reliability, considering their impact on computational resources."
Prerequisites
Working proficiency with Python on topics covered in the Google Crash Course on Python.
Prior experience with foundational machine learning concepts and deep learning models, as well as familiarity with model evaluation, bias-variance tradeoff, overfitting, and regularization techniques are recommended.
Audience
Data Engineers, Data Scientists, ML Engineers, and Software Engineers
Available languages
English
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Explore Google Cloud content on Coursera and Pluralsight.
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View the public classroom schedule here.
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