| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250228b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783031469893 |
| Terms of availability |
(hbk) |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
HOS |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hossain, Eklas |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning crash course for engineers |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2024 |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 453 p. ; |
| Other physical details |
ill., (chiefly col.), |
| Dimensions |
25 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
59.99 |
| Price type code |
€ |
| Unit of pricing |
93.20 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly. A concise guide to the basics of algorithms, building models, and performance evaluation; Offers highly illustrated, step-by-step guidelines with Python programming examples; Provides examples and exercises related to signal and image processing, energy systems, and robotics. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Chebyshev distance |
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| Topical term or geographic name as entry element |
Computer vision |
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| Topical term or geographic name as entry element |
Cosine similarity |
|
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| Topical term or geographic name as entry element |
Data points |
|
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| Topical term or geographic name as entry element |
Euclidean distance |
|
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| Topical term or geographic name as entry element |
Hamming distance |
|
|---|
| Topical term or geographic name as entry element |
Loss function |
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| Topical term or geographic name as entry element |
Manhattan distance |
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| Topical term or geographic name as entry element |
Random forest |
|
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| Topical term or geographic name as entry element |
Train Epoch |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |