May 1, 2024
3 minute read
AutoML Vision is a cloud-based service powered by Google AI that enables developers with limited machine learning expertise to train high-quality image classification models. With AutoML Vision, you can create models to identify objects, classify images into predefined categories, detect sentiment, and search for similar images. This makes it easy to add image recognition capabilities to your applications without having to build and train complex machine learning models from scratch.
Why Learn AutoML Vision?
There are several reasons why you might want to learn AutoML Vision:
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Find a path to becoming a AutoML Vision. Learn more at:
OpenCourser.com/topic/pswa7u/automl
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AutoML Vision.
This comprehensive textbook provides a solid foundation in computer vision concepts and techniques, including image formation, feature extraction, image segmentation, object recognition, and motion analysis. It valuable resource for students, researchers, and practitioners in the field of computer vision and image processing.
Covers a wide range of machine learning topics, including AutoML Vision. It provides a solid foundation in machine learning concepts and valuable resource for developers who want to learn more about AutoML Vision and its applications.
Provides a comprehensive overview of deep learning, including a chapter on computer vision. While it does not cover AutoML Vision specifically, it provides a strong foundation in the underlying concepts that power AutoML Vision.
This foundational textbook introduces the core concepts and algorithms of computer vision. It provides a comprehensive overview of the field, from image formation to object recognition and scene understanding.
This textbook provides a comprehensive overview of machine learning techniques for computer vision tasks. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
This classic textbook provides a comprehensive introduction to pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and Bayesian methods.
Covers computer vision topics, including image classification, object detection, and image segmentation. While it does not cover AutoML Vision specifically, it provides a good overview of the computer vision techniques that are used in AutoML Vision.
This widely acclaimed textbook provides a comprehensive overview of artificial intelligence, including topics such as machine learning, computer vision, and natural language processing.
Provides a practical introduction to computer vision techniques for game development. It covers topics such as image processing, object recognition, and motion tracking.
Provides a comprehensive overview of computer vision techniques for visual effects. It covers topics such as image compositing, motion tracking, and lighting effects.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/pswa7u/automl
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