(Ebook) Deep Learning Patterns and Practices by Andrew Ferlitsch ISBN 9781617298264, 1617298263

(Ebook) Deep Learning Patterns and Practices by Andrew Ferlitsch ISBN 9781617298264, 1617298263

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 environmentThe 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.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 bookDeep 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 visionAbout the reader For machine learning engineers familiar with Python and deep learning.About the authorAndrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations.Table of ContentsPART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline