Practical Machine Learning for Computer Vision - by Valliappa Lakshmanan & Martin Görner & Ryan Gillard (Paperback)

This book is a comprehensive guide to employing machine learning models for image analysis, providing practical techniques for solving various computer vision tasks such as classification, object detection, autoencoders, image generation, counting, and captioning.

The authors, experienced Google engineers, demonstrate how to design, train, evaluate, and deploy accurate and explainable ML models using TensorFlow or Keras.

The book covers the entire ML pipeline, from dataset creation to model deployment, and includes best practices for incorporating explainability and responsible AI.

With real-world examples and case studies, this book is an essential resource for ML engineers and data scientists looking to develop and deploy image analysis models in a flexible and maintainable way.