Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.InDeep Learning Patterns and Practicesyou will learn:Internal functioning of modern convolutional neural networksProcedural reuse design pattern for CNN architecturesModels for mobile and IoT devicesAssembling large-scale model deploymentsOptimizing hyperparameter tuningMigrating a model to a production environmentThe big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production.Deep Learning Patterns and Practicesis 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyDiscover 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 bookDeep Learning Patterns and Practicesis 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 insideModern convolutional neural networksDesign pattern for CNN architecturesModels for mobile and IoT devicesLarge-scale model deploymentsExamples for computer visionAbout the readerFor machine learning engineers familiar with Python and deep learning.About the authorAndrew Ferlitschis an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations.Table of ContentsPART 1 DEEP LEARNING FUNDAMENTALS1 Designing modern machine learning2 Deep neural networks3 Convolutional and residual neural networks4 Training fundamentalsPART 2 BASIC DESIGN PATTERN5 Procedural design pattern6 Wide convolutional neural networks7 Alternative connectivity patterns8 Mobile convolutional neural networks9 AutoencodersPART 3 WORKING WITH PIPELINES10 Hyperparameter tuning11 Transfer learning12 Data distributions13 Data pipeline14 Training and deployment pipeline