000			02000cam a2200229 i 4500
008			220901s2021 cc a b 001 0 eng
020			_a9789391043834
042			_apcc
082	0	4	_a005.74015 _bLAK
100	1		_aLakshmanan, Valliappa,
245	1	0	_aPractical machine learning for computer vision : _bend-to-end machine learning for images
250			_aFirst edition.
260			_aMumbai _bShroff Publishers & Distributors Pvt. Ltd. _c2021
300			_axvi, 463 pages :
520			_aThis 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 pre-processing, 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 Pre-process 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
650		0	_aComputer vision.
650		0	_aMachine learning.
700	1		_aGörner, Martin,
700	1		_aGillard, Ryan,
906			_aBusiness Analytics
942			_2ddc _c1
999			_c98126 _d98126