Practical Machine Learning for Computer Vision (1 ed)

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems, including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book offers a comprehensive introduction to end-to-end deep learning, covering dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard demonstrate how to develop accurate and explainable computer vision ML models and deploy them in large-scale production using a robust ML architecture in a flexible and maintainable manner. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.

You'll learn how to:

• Design ML architecture for computer vision tasks
• Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
• Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
• Preprocess images for data augmentation and to support learnability
• Incorporate explainability and responsible AI best practices
• Deploy image models as web services or on edge devices
• Monitor and manage ML models