- Home
- Computers
- Intelligence (AI) & Semantics
- Deep Learning Patterns and Practices
Deep Learning Patterns and Practices
List Price:
$59.99
- Availability: Confirm prior to ordering
- Branding: minimum 50 pieces (add’l costs below)
- Check Freight Rates (branded products only)
Branding Options (v), Availability & Lead Times
- 1-Color Imprint: $2.00 ea.
- Promo-Page Insert: $2.50 ea. (full-color printed, single-sided page)
- Belly-Band Wrap: $2.50 ea. (full-color printed)
- Set-Up Charge: $45 per decoration
- Availability: Product availability changes daily, so please confirm your quantity is available prior to placing an order.
- Branded Products: allow 10 business days from proof approval for production. Branding options may be limited or unavailable based on product design or cover artwork.
- Unbranded Products: allow 3-5 business days for shipping. All Unbranded items receive FREE ground shipping in the US. Inquire for international shipping.
- RETURNS/CANCELLATIONS: All orders, branded or unbranded, are NON-CANCELLABLE and NON-RETURNABLE once a purchase order has been received.
Product Details
Author:
Andrew Ferlitsch
Format:
Paperback
Pages:
472
Publisher:
Manning (October 5, 2021)
Language:
English
ISBN-13:
9781617298264
ISBN-10:
1617298263
Weight:
25.44oz
Dimensions:
7.375" x 9.25" x 1"
File:
Eloquence-SimonSchuster_08192026_P10502905_onix30-20260819.xml
Folder:
Eloquence
List Price:
$59.99
Case Pack:
18
As low as:
$53.99
Publisher Identifier:
P-SS
Discount Code:
G
Pub Discount:
37
Imprint:
Manning
Overview
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
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
