Practical MATLAB Deep Learning : A Projects-Based Approach / Michael Paluszek, Stephanie Thomas, Eric Ham

Von:

Paluszek, Michael [VerfasserIn]

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Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer eBook CollectionVerlag: [Berkeley, CA] : Apress, 2022Auflage: 2nd ed. 2022Beschreibung: 1 Online-Ressource (XIX, 329 Seiten) : IllustrationenISBN:

9781484279120

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Andere physische Formen: 9781484279113 | 9781484279137 | Erscheint auch als: 9781484279113 Druck-Ausgabe | Erscheint auch als: 9781484279137 Druck-Ausgabe | Erscheint auch als: 9781484291955 Druck-AusgabeDOI: DOI: 10.1007/978-1-4842-7912-0Online-Ressourcen:

Zusammenfassung: 1. What is deep learning? -- 2. MATLAB Toolboxes -- 3. Finding Circles -- 4. Classifying Movies -- 5. Algorithmic Deep Learning -- 6. Tokamak Disruption Detection -- 7. Classifying a Pirouette -- 8. Completing Sentences -- 9. Terrain Based Navigation -- 10. Stock Prediction -- 11. Image Classification -- 12. Orbit Determination -- 13. Earth Sensors -- 14. Generative Modeling of Music -- 15. Reinforcement Learning -- Bibliography.Zusammenfassung: Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to dance physics You will: Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction.PPN: PPN: 1816514799Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-CWD | ZDB-2-SXPC

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