Description

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

Book Details

   Language: English
   Published: 2021
   ISBN: 1098102363
   Format: True PDF