Mastering machine learning algorithms : (Record no. 21025)
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000 -LEADER | |
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fixed length control field | 01922nam a2200229Ia 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | NULRC |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20250520103011.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 250520s9999 xx 000 0 und d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9781838820299 |
040 ## - CATALOGING SOURCE | |
Transcribing agency | NULRC |
050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | Q 325.5 .B66 2020 |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Bonaccorso, Giuseppe |
Relator term | author |
245 #0 - TITLE STATEMENT | |
Title | Mastering machine learning algorithms : |
Remainder of title | expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work / |
Statement of responsibility, etc. | Giuseppe Bonaccorso |
250 ## - EDITION STATEMENT | |
Edition statement | Second Edition |
260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
Place of publication, distribution, etc. | Birmingham, UK : |
Name of publisher, distributor, etc. | Packt Publishing, Limited, |
Date of publication, distribution, etc. | c2020 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | xviii, 773 pages ; |
Dimensions | 24 cm. |
365 ## - TRADE PRICE | |
Price amount | USD45 |
505 ## - FORMATTED CONTENTS NOTE | |
Formatted contents note | Machine Learning Model Fundamentals -- Loss functions and Regularization -- Introduction to Semi-Supervised Learning -- Advanced Semi-Supervised Classification -- Graph-based Semi-Supervised Learning -- Clustering and Unsupervised Models -- Advanced Clustering and Unsupervised Models -- Clustering and Unsupervised Models for Marketing -- Generalized Linear Models and Regression -- Introduction to Time-Series Analysis -- Bayesian Networks and Hidden Markov Models -- The EM Algorithm -- Component Analysis and Dimensionality Reduction -- Hebbian Learning -- Fundamentals of Ensemble Learning -- Advanced Boosting Algorithms -- Modeling Neural Networks -- Optimizing Neural Networks -- Deep Convolutional Networks -- Recurrent Neural NetworksAuto-Encoders -- Introduction to Generative Adversarial Networks -- Deep Belief Networks -- Introduction to Reinforcement Learning -- Advanced Policy Estimation Algorithms. |
520 ## - SUMMARY, ETC. | |
Summary, etc. | A new second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems, updated to include Python 3.8 and TensorFlow 2.x as well as the latest in new algorithms and techniques |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | COMPUTER ALGORITHMS |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | Library of Congress Classification |
Koha item type | Books |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type |
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Library of Congress Classification | Gen. Ed. - CCIT | LRC - Main | National University - Manila | General Circulation | 12/03/2022 | Purchased - Amazon | 45.00 | GC Q 325.5 .B66 2020 | NULIB000018784 | 05/20/2025 | c.1 | 05/20/2025 | Books |