Preface

Deep Learning (DL) is a relatively new type of machine learning which demonstrates incredible results in tasks such as natural language understanding and computer vision. At times, DL can be more accurate than humans.

Thanks to the proliferation of open source frameworks, publicly available model architectures and pertained models, many people and organizations can successfully apply cutting-edge DL models to their practical use cases. However, developing, training, and deploying DL models also requires highly specialized and costly types of hardware, software stacks, expertise, and management capabilities which may considerably slow down the adoption.

This book focuses on how to engineer and manage Deep Learning workloads on Amazon ...

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