MATLAB machine learning recipes: a problem-solution approach
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular pr...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
[2024]
|
| Ausgabe: | Third edition |
| Schlagworte: | |
| Zusammenfassung: | Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What You Will Learn Write code for machine learning, adaptive control, and estimation using MATLAB Use MATLAB graphics and visualization tools for machine learning Become familiar with neural nets Build expert systems Understand adaptive control Gain knowledge of Kalman Filters Who This Book Is For Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists |
| Beschreibung: | XXIII, 447 Seiten Diagramme |
| ISBN: | 9781484298459 |
Internformat
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Datensatz im Suchindex
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| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
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| id | DE-604.BV050060379 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-28T11:08:56Z |
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| isbn | 9781484298459 |
| language | English |
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| physical | XXIII, 447 Seiten Diagramme |
| publishDate | 2024 |
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| publisher | Apress |
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| spelling | Paluszek, Michael 1954- Verfasser (DE-588)1093613025 aut MATLAB machine learning recipes a problem-solution approach Michael Paluszek, Stephanie Thomas Third edition Berkeley, CA Apress [2024] XXIII, 447 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable. You can easily look up a particular problem and follow the steps in the solution. This book has something for everyone interested in machine learning. It also has material that will allow those with an interest in other technology areas to see how machine learning and MATLAB can help them solve problems in their areas of expertise. The chapter on data representation and MATLAB graphics includes new data types and additional graphics. Chapters on fuzzy logic, simple neural nets, and autonomous driving have new examples added. And there is a new chapter on spacecraft attitude determination using neural nets. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow you to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What You Will Learn Write code for machine learning, adaptive control, and estimation using MATLAB Use MATLAB graphics and visualization tools for machine learning Become familiar with neural nets Build expert systems Understand adaptive control Gain knowledge of Kalman Filters Who This Book Is For Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists Artificial Intelligence Big Data Artificial intelligence Big data MATLAB (DE-588)4329066-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf MATLAB. Machine learning Apprentissage automatique MATLAB (DE-588)4329066-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Thomas, Stephanie Verfasser (DE-588)1093613823 aut Erscheint auch als Online-Ausgabe 978-1-4842-9846-6 |
| spellingShingle | Paluszek, Michael 1954- Thomas, Stephanie MATLAB machine learning recipes a problem-solution approach Artificial Intelligence Big Data Artificial intelligence Big data MATLAB (DE-588)4329066-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4329066-8 (DE-588)4193754-5 |
| title | MATLAB machine learning recipes a problem-solution approach |
| title_auth | MATLAB machine learning recipes a problem-solution approach |
| title_exact_search | MATLAB machine learning recipes a problem-solution approach |
| title_full | MATLAB machine learning recipes a problem-solution approach Michael Paluszek, Stephanie Thomas |
| title_fullStr | MATLAB machine learning recipes a problem-solution approach Michael Paluszek, Stephanie Thomas |
| title_full_unstemmed | MATLAB machine learning recipes a problem-solution approach Michael Paluszek, Stephanie Thomas |
| title_short | MATLAB machine learning recipes |
| title_sort | matlab machine learning recipes a problem solution approach |
| title_sub | a problem-solution approach |
| topic | Artificial Intelligence Big Data Artificial intelligence Big data MATLAB (DE-588)4329066-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Artificial Intelligence Big Data Artificial intelligence Big data MATLAB Maschinelles Lernen |
| work_keys_str_mv | AT paluszekmichael matlabmachinelearningrecipesaproblemsolutionapproach AT thomasstephanie matlabmachinelearningrecipesaproblemsolutionapproach |