Requirements: .ePUB reader, 15.8 MB
Overview: Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack Deep Learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack Deep Learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it’s effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. This book aims to help you develop a deeper knowledge of the Deep Learning stack—specifically, how Deep Learning interfaces with hardware, software, and data. It will serve as a valuable resource when you want to scale your Deep Learning model, either by expanding the hardware resources or by adding larger volumes of data or increasing the capacity of the model itself. This book is written for Machine Learning practitioners from all walks of life: engineers, data engineers, MLOps, Deep Learning scientists, Machine Learning engineers, and others interested in learning about model development at scale. It assumes that the reader already has a fundamental knowledge of deep learning concepts such as optimizers, learning objectives and loss functions, and model assembly and compilation, as well as some experience with model development. Familiarity with Python and PyTorch is also essential for the practical sections of the book.
Genre: Non-Fiction > Tech & Devices
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