{"product_id":"deep-learning-patterns-and-practices-9781617298264","title":"Deep Learning Patterns and Practices","description":"\u003cp\u003eDiscover 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\u0026amp;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 Ferlitschs work with Google Cloud AI. In it  youll 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 thats 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. Youll 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. Youll 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  youll get tips for deploying  testing  and maintaining your projects.  Whats 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.  Table of Contents  PART 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\u003c\/p\u003e","brand":"My Store","offers":[{"title":"Default Title","offer_id":67894459891765,"sku":"ByrdShop_1617298263","price":40.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0627\/8139\/0901\/files\/9781617298264.jpg?v=1793765560","url":"https:\/\/atxbooks.com\/products\/deep-learning-patterns-and-practices-9781617298264","provider":"ATX Books","version":"1.0","type":"link"}