Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images (Paperback)

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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 G rner, 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


Product Details
ISBN: 9781098102364
ISBN-10: 1098102363
Publisher: O'Reilly Media
Publication Date: August 10th, 2021
Pages: 482
Language: English