Mastering machine learning algorithms : (Record no. 4987)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02189nam a2200217Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUBLRC |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241210s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-1-83882-029-9 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | NUBLRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | GC Q 325 .B66 2020 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bonaccorso, Giuseppe |
| 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. | Guiseppe Bonaccorso |
| 250 ## - EDITION STATEMENT | |
| Edition statement | Second Edition. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | United Kingdom ; |
| Name of publisher, distributor, etc. | Packt Publishing, |
| Date of publication, distribution, etc. | c2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xviii, 773 pages : |
| Other physical details | illustrations ; |
| Dimensions | 24cm. |
| 365 ## - TRADE PRICE | |
| Price amount | USD 49.99 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Include index |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 1. Machine Learning Model Fundamentals -- Chapter 2. Loss functions and Regularization -- Chapter 3. Introduction to Semi-Supervised Learning -- Chapter 4. Advanced Semi-Supervised Classifiation -- Chapter 5. Graph-based Semi-Supervised Learning -- Chapter 6. Clustering and Unsupervised Models -- Chapter 7. Advanced Clustering and Unsupervised Models -- Chapter 8. Clustering and Unsupervised Models for Marketing -- Chapter 9. Generalized Linear Models and Regression -- Chapter 10. Introduction to Time-Series Analysis -- Chapter 11. Bayesian Networks and Hidden Markov Models -- Chapter 12. The EM Algorithm -- Chapter 13. Component Analysis and Dimensionality Reduction -- Chapter 14. Hebbian Learning -- Chapter 15. Fundamentals of Ensemble Learning -- Chapter 16. Advanced Boosting Algorithms -- Chapter 17. Modeling Neural Networks -- Chapter 18. Optimizing Neural Networks -- Chapter 19. Deep Convolutional Networks -- Chapter 20. Recurrent Neural Networks -- Chapter 21. Auto-Encoders -- Chpater 22. Introduction to Generative Adversarial Networks -- Chapter 23. Deep Belief Networks -- Chapter 24. Introduction to Reinforcement Learning -- Chapter 25. 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. |
| 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 | Total checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library of Congress Classification | Information Technology | NU BALIWAG | NU BALIWAG | General Circulation | Purchased - Amazon | GC Q 325 .B66 2020 | NUBUL000004422 | 12/12/2024 | 12/12/2024 | Books |
