Practical Machine Learning for Computer Vision : End-to-End Machine Learning for Images
Language: English
Published by O'reilly Media Aug 2021, 2021
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 163.84
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Add to basketItem description from seller
Neuware - 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 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. 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. …
Seller Inventory # 9781098102364
- Title
- Practical Machine Learning for Computer Vision : End-to-End Machine Learning for Images
- Author
- Valliappa Lakshmanan
- Publisher
- O'reilly Media Aug 2021
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1098102363
- ISBN 13
- 9781098102364
- Item weight
- 849 grams
- Dimensions
- 236x179x31 mm
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 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.
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
"Synopsis" may belong to another edition of this title.
About the Author
Martin Görner 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.
"About the title" may belong to another edition of this title.
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