Practical Machine Learning for Computer Vision

margin: 0px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'You'll learn how to: