MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02981nam a2200277 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
CSPC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20241007132135.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
241007s2022 wau 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781484274125 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CSPC |
| Language of cataloging |
eng |
| Transcribing agency |
CSPC |
| Description conventions |
rda |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.73 |
| Item number |
.Y42 2022 |
| 082 #4 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23 |
| Classification number |
006.31 |
| Item number |
Y3m |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ye, Andre, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Modern deep learning design and application development : |
| Remainder of title |
versatile tools to solve deep learning problems / |
| Statement of responsibility, etc. |
Andre Ye. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Redmond, WA : |
| Name of producer, publisher, distributor, manufacturer |
Apress, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 451 pages ; |
| Dimensions |
25 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
A deep dive into keras -- Pretraining strategies and transfer learning -- The versatility of autoencoders -- Model compression for practical deployment -- Automating model design with meta-optimization -- Successful neural network architecture design -- Reframing difficult deep learning problems. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. Youll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, youll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. Youll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to todays difficult problems. You will: Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Deep learning (Machine learning). |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Suppress in OPAC |
No |
| Koha item type |
Books |
| Edition |
23 |
| Classification part |
006.31 |
| Call number prefix |
GRD |
| Call number suffix |
2022 |
