Deep Learning Design Patterns

Andrew Ferlitsch

ISBN 10: 1617298263 / ISBN 13: 9781617298264

Editore: Manning Publications Nov 2021, 2021

Riguardo questo articolo

Neuware - 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 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. Purchase of the print book includes a free Elektronisches Buch 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 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. 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 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.

Codice articolo 9781617298264

Dati bibliografici

• Titolo: Deep Learning Design Patterns
• Editore: Manning Publications Nov 2021
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1617298263
• ISBN 13: 9781617298264
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 187 A x 32 P (millimetri) 235 millimetri di larghezza per 187 millimetri di altezza per 32 millimetri di profondità
• Peso: 884 grammi

Descrizione articolo

Riassunto

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 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.

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 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.

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 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

Informazioni sull?autore

Andrew Ferlitsch 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.