Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.
In Deep Learning Patterns and Practices you will learn:
Internal functioning of modern convolutional neural networks
Procedural reuse design pattern for CNN architectures
Models for mobile and IoT devices
Assembling large-scale model deployments
Optimizing hyperparameter tuning
Migrating a model to a production environment
The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples.
about the technology
Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example.
about the book
Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects.
what's inside
Modern convolutional neural networks
Design pattern for CNN architectures
Models for mobile and IoT devices
Large-scale model deployments
Examples for computer vision
about the reader
For machine learning engineers familiar with Python and deep learning.
about the author
Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations.
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One of the best deep learning books I have read.
A must-read for anyone trying to understand the ins and outs of deep learning and best practices for building a machine learning pipeline.
Really good for the modern deep learning professional. Suggested for those who want to understand what’s under the hood.
Whether you are new to AI or have some knowledge of it, this will be your companion on the path to expertise.
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Systematic pragmatic approach to deep learning
November 17, 2021 by Kevin S. (CT, US)
“I have read several deep learning books. This gives very current, pragmatic, detailed advice on the various options for different machine learning tasks. Code snippets are well structured and numerous. Flows well. While it may not require prior knowledge of deep learning, I have to assume it would make more sense to someone with some prior introduction to the field. It gives a systematic overall structure and approach. Highly recommended.”
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July 29, 2021 by Dave (New Zealand)
“Definitive guide to the history, architecture, development and deployment of deep learning models with particular emphasis on convnets”
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October 8, 2020 by George (US)
“Manning is a great publisher of amazing content!”
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A must-read for anyone responsible for applications of deep learning
September 8, 2020 by A Reviewer (CA)
“This is the best book on deep learning architectures currently available. A must-read for anyone responsible for applications of deep learning or needing to assimilate the mass of techniques that have made it so suvccessful. It is an outstanding example of how modern software engineering concepts can be applied to systematize a new technology that is rapidly evolving and difficult to comprehend.”
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