Practical Machine Learning for Computer Vision

eBook Details:

• Paperback: 482 pages
• Publisher: WOW! eBook (August 17, 2021)
• Language: English
• ISBN-10: 1098102363
• ISBN-13: 978-1098102364

eBook Description:

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

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 Practical Machine Learning for Computer Vision book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.

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

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

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