Practical Machine Learning for Computer Vision

Valliappa Lakshmanan

ISBN 10: 1098102363 / ISBN 13: 9781098102364

Edité par O'reilly Media Sep 2021, 2021

A propos de cet article

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.

N° de réf. du vendeur 9781098102364

Détails bibliographiques

• Titre: Practical Machine Learning for Computer Vision
• Éditeur: O'reilly Media Sep 2021
• Date de parution: 2021
• Langue: anglais
• ISBN à 10 chiffres: 1098102363
• ISBN à 13 chiffres: 9781098102364
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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

À propos des auteurs

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 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.