Deep Learning Patterns and Practices - by Andrew Ferlitsch (Paperback)
Similar Products
Products of same category from the store
All
Product info
<p/><br></br><p><b> About the Book </b></p></br></br>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. 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.<p/><br></br><p><b> Book Synopsis </b></p></br></br><b>Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.</b> <p/>In <i>Deep Learning Patterns and Practices</i> you will learn: <p/> Internal functioning of modern convolutional neural networks<br> Procedural reuse design pattern for CNN architectures<br> Models for mobile and IoT devices<br> Assembling large-scale model deployments<br> Optimizing hyperparameter tuning<br> Migrating a model to a production environment <p/>The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. <i>Deep Learning Patterns and Practices</i> 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. <p/>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. <p/> About the technology<br> 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. <p/>About the book<br> <i>Deep Learning Patterns and Practices</i> 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. <p/> What's inside <p/> Modern convolutional neural networks<br> Design pattern for CNN architectures<br> Models for mobile and IoT devices<br> Large-scale model deployments<br> Examples for computer vision <p/>About the reader<br> For machine learning engineers familiar with Python and deep learning. <p/>About the author<br> <b>Andrew Ferlitsch</b> is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. <p/>Table of Contents <p/>PART 1 DEEP LEARNING FUNDAMENTALS<br> 1 Designing modern machine learning<br> 2 Deep neural networks<br> 3 Convolutional and residual neural networks<br> 4 Training fundamentals<br> PART 2 BASIC DESIGN PATTERN<br> 5 Procedural design pattern<br> 6 Wide convolutional neural networks<br> 7 Alternative connectivity patterns<br> 8 Mobile convolutional neural networks<br> 9 Autoencoders<br> PART 3 WORKING WITH PIPELINES<br> 10 Hyperparameter tuning<br> 11 Transfer learning<br> 12 Data distributions<br> 13 Data pipeline<br> 14 Training and deployment pipeline<p/><br></br><p><b> About the Author </b></p></br></br><b>Andrew Ferlitsch</b> is an expert on computer vision and deep learning at Google Cloud AI Developer Relations. He was formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he amassed 115 US patents and worked on emerging technologies in telepresence, augmented reality, digital signage, and autonomous vehicles. In his present role, he reaches out to developer communities, corporations and universities, teaching deep learning and evangelizing Google's AI technologies.
Price History
Cheapest price in the interval: 59.99 on October 27, 2021
Most expensive price in the interval: 59.99 on December 20, 2021
Price Archive shows prices from various stores, lets you see history and find the cheapest. There is no actual sale on the website. For all support, inquiry and suggestion messagescommunication@pricearchive.us