Practical Machine Learning for Computer Vision

4.50 (8 ratings by Goodreads)
Regular price €84.99
A01=Martin Goerner
A01=Ryan Gillard
A01=Valliappa Lakshmanan
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AI artificial intelligence Machine learning deep learning image understanding practical ML computer vision edge ML
Author_Martin Goerner
Author_Ryan Gillard
Author_Valliappa Lakshmanan
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Category1=Non-Fiction
Category=UN
COP=United States
Delivery_Delivery within 10-20 working days
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eq_non-fiction
Language_English
PA=Available
Price_€50 to €100
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softlaunch

Product details

  • ISBN 9781098102364
  • Dimensions: 178 x 233mm
  • Publication Date: 31 Aug 2021
  • Publisher: O'Reilly Media
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
Delivery/Collection within 10-20 working days

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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 provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Goerner, 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. 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
Valliappa (Lak) Lakshmanan is the director of analytics and AI solutions at Google Cloud, where he leads a team building cross-industry solutions to business problems. His mission is to democratize machine learning so that it can be done by anyone anywhere. Martin Goerner is a product manager for Keras/TensorFlow focused on improving the developer experience when using state-of-the-art models. He's passionate about science, technology, coding, algorithms, and everything in between. Ryan Gillard is an AI engineer in Google Cloud's Professional Services organization, where he builds ML models for a wide variety of industries. He started his career as a research scientist in the hospital and healthcare industry. With degrees in neuroscience and physics, he loves working at the intersection of those disciplines exploring intelligence through mathematics.