Deep learning patterns and practices, Andrew Ferlitsch
Type
Label
Deep learning patterns and practices, Andrew Ferlitsch
Language
eng
Characteristic
videorecording
Main title
Deep learning patterns and practices
Oclc number
11312650879
Responsibility statement
Andrew Ferlitsch
Runtime
835
Summary
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video. 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 find: 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. about the audience 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. NARRATED BY MARK THOMAS
Technique
live action