MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04647nam a22002777a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
ZW-GwMSU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230818123113.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230630b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119543046 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
rda |
| Language of cataloging |
English |
| Description conventions |
rda |
| Transcribing agency |
MSULIB |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 MUE |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mueller, John Paul. |
| Relator term |
author |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Deep learning for dummies / |
| Statement of responsibility, etc |
created by John Paul Mueller and Luca Massaron. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Name of producer, publisher, distributor, manufacturer |
John Wiley & Sons, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2019 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 350 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm |
| 336 ## - CONTENT TYPE |
|---|
| content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| media type term |
unmediated |
| media type code |
n |
| source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| carrier type term |
volume |
| carrier type code |
nc |
| source |
rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes index |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Intro; Title Page; Copyright Page; Table of Contents; Introduction; About This Book; Foolish Assumptions; Icons Used in This Book; Beyond the Book; Where to Go from Here; Part 1 Discovering Deep Learning; Chapter 1 Introducing Deep Learning; Defining What Deep Learning Means; Starting from Artificial Intelligence; Considering the role of AI; Focusing on machine learning; Moving from machine learning to deep learning; Using Deep Learning in the Real World; Understanding the concept of learning; Performing deep learning tasks; Employing deep learning in applications Considering the Deep Learning Programming EnvironmentOvercoming Deep Learning Hype; Discovering the start-up ecosystem; Knowing when not to use deep learning; Chapter 2 Introducing the Machine Learning Principles; Defining Machine Learning; Understanding how machine learning works; Understanding that it's pure math; Learning by different strategies; Training, validating, and testing data; Looking for generalization; Getting to know the limits of bias; Keeping model complexity in mind; Considering the Many Different Roads to Learning; Understanding there is no free lunch Discovering the five main approachesDelving into some different approaches; Awaiting the next breakthrough; Pondering the True Uses of Machine Learning; Understanding machine learning benefits; Discovering machine learning limits; Chapter 3 Getting and Using Python; Working with Python in this Book; Obtaining Your Copy of Anaconda; Getting Continuum Analytics Anaconda; Installing Anaconda on Linux; Installing Anaconda on MacOS; Installing Anaconda on Windows; Downloading the Datasets and Example Code; Using Jupyter Notebook; Defining the code repository; Getting and using datasets Creating the ApplicationUnderstanding cells; Adding documentation cells; Using other cell types; Understanding the Use of Indentation; Adding Comments; Understanding comments; Using comments to leave yourself reminders; Using comments to keep code from executing; Getting Help with the Python Language; Working in the Cloud; Using the Kaggle datasets and kernels; Using the Google Colaboratory; Chapter 4 Leveraging a Deep Learning Framework; Presenting Frameworks; Defining the differences; Explaining the popularity of frameworks; Defining the deep learning framework Choosing a particular frameworkWorking with Low-End Frameworks; Caffe2; Chainer; PyTorch; MXNet; Microsoft Cognitive Toolkit/CNTK; Understanding TensorFlow; Grasping why TensorFlow is so good; Making TensorFlow easier by using TFLearn; Using Keras as the best simplifier; Getting your copy of TensorFlow and Keras; Fixing the C++ build tools error in Windows; Accessing your new environment in Notebook; Part 2 Considering Deep Learning Basics; Chapter 5 Reviewing Matrix Math and Optimization; Revealing the Math You Really Need; Working with data; Creating and operating with a matrix |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic-and all of the underlying technologies associated with it. In no time, you'll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.-Includes sample code -Provides real-world examples within the approachable text -Offers hands-on activities to make learning easier -Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Book |