| 000 -LEADER |
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| fixed length control field |
03677nam a22002537a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20220429051317.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220429b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119602873 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CvSU-CCAT Campus Library. |
| Language of cataloging |
English. |
| Transcribing agency |
CvSU-CCAT Campus Library. |
| Description conventions |
rda. |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q 325.5 |
| Item number |
M57 2020 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mishra, Abhishek, author. |
| 9 (RLIN) |
4923 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning for iOS developers / |
| Statement of responsibility, etc. |
Abhishek Mishra. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
[Place of publication not identified] : |
| Name of publisher, distributor, etc. |
John Wiey & Sons, Inc., |
| Date of publication, distribution, etc. |
c2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi, 327 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
23 cm |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Part 1 : Fundamentals of machine learning<br/>Chapter 1 : Introduction to machine learning<br/>Chapter 2 : The machine-learning approach<br/>Chapter 3 : Data exploration and preprocessing<br/>Chapter 4 : Implementing machine learning on mobile apps<br/>Part 2 : Machine learning with coreML, CreateML, and TuriCreate<br/>Chapter 5 : Object detection using pre-trained models<br/>Chapter 6 : Creating an image classifier with the Create ML app<br/>Chapter 7 : Creating a tabular classifier with Create ML<br/>Chapter 8 : Creating a decision tree classifier<br/>Chapter 9 : Creating a logistic regression model using Scikit-learn and Core ML<br/>Chapter 10 : Building a deep convolutional neural network with Keras<br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models'both pre-trained and user-built'with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: -Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics -Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming -Develop skills in data acquisition and modeling, classification, and regression.-Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) -Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn' & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
In English text. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning. |
| 9 (RLIN) |
301 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computers. |
| 9 (RLIN) |
414 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Book |
| Classification part |
Q 325.5 M57 2020 |
| Call number prefix |
CIR |
