| 000 -LEADER |
|---|
| fixed length control field |
02733nam a22003257a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260928103343.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260928b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789819814091 [paperback] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Liu, G. R., |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning with python : |
| Remainder of title |
theory and applications / |
| Statement of responsibility, etc |
G. R. Liu. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Singapore : |
| Name of publisher, distributor, etc |
World Scientific Publishing Co. Pte. Ltd., |
| Date of publication, distribution, etc |
c2023. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxii, 670 pages : |
| Other physical details |
colored illustrations ; |
| Dimensions |
24.4 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 |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references, index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Contents: About the author — 1. Introduction — 2. Basics of python — 3. Basic mathematical computations — 4. Statistics and probability-based learning model — 5. Prediction function and universal prediction theory — 6. The perceptron and SVM — 7. Activation functions and universal approximation theory — 8. Automatic differentiation and autograd — 8. Solution existence theory and optimization techniques — 10 Loss function for regression — 11 Loss functions and models for classification — 12. Multiclass classification — 13 Multilayer perceptron (MLP) for regression and classification — 14 Overfitting and regularization — 15. Convulutional neural network (CNN) for classification and object detection — 16. Recurrent neural network (RNN) and sequence feature models — 17 Unsupervised learning techniques — 18 Reinforcement learning (RL) — Index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"Machine Learning (ML) has become a very important area of research widely used in various industries.<br/><br/>This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.<br/><br/>The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks." —Provided by the publisher |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Purchased |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Source of heading or term |
Machine learning |
| General subdivision |
Data processing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Artificial intelligence |
| General subdivision |
Mathematical models. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Subject Reference |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Gian [new] |
| Date encoded |
09/28/2026 |
