Practical Machine Learning for Computer Vision

The "Image Processing with Machine Learning" book is a comprehensive guide to employing machine learning models for image analysis.

With this practical resource, you'll learn how to solve various image problems using proven ML techniques, including classification, object detection, autoencoders, image generation, counting, and captioning.

The authors, Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard, provide a step-by-step approach to developing accurate and explainable computer vision models and deploying them in large-scale production using robust ML architecture.

You'll discover how to design ML architectures for computer vision tasks, select the right model, create an end-to-end ML pipeline, preprocess images for data augmentation, incorporate explainability best practices, and deploy models as web services or on edge devices.

This book is a must-read for ML engineers and data scientists looking to improve their skills in image processing and machine learning.