115,59 €
Practical Machine Learning for Computer Vision
Practical Machine Learning for Computer Vision
115,59 €
  • Išsiųsime per 12–16 d.d.
By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques.Google engineers Valliappa Lakshmanan, Martin Garner, and Ryan Gillard show you how to develop acc…
115.59

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By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques.

Google engineers Valliappa Lakshmanan, Martin Garner, 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/Keras. This book also covers best practices to improve the operationalization of the models using end-to-end ML pipelines.

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
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Formatai:

115,59 € Nauja knyga
minkšti viršeliai

By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques.

Google engineers Valliappa Lakshmanan, Martin Garner, 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/Keras. This book also covers best practices to improve the operationalization of the models using end-to-end ML pipelines.

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

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