Practical MATLAB deep learning : a project-based approach / Michael Paluszek, Stephanie Thomas
By: Paluszek, Michael.
Contributor(s): Thomas, Stephanie | Ohio Library and Information Network.
Publisher: Berkeley, CA : Apress, ©2020Description: 260 p.Content type: text Media type: computer Carrier type: online resourceISBN: 9781484251232.Subject(s): Machine learningGenre/Form: Print books.Summary: Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. Youll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. Youll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. 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| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q325.5 .P35 2020 (Browse shelf) | Available | AU00000000016950 |
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| Q325.5 .M87 2012 Machine learning : a probabilistic perspective / | Q325.5 .N35 2022 Interpreting machine learning models : learn model interpretability and explainability methods / | Q325.5 .N54 2019 Practical time series analysis : prediction with statistics and machine learning / | Q325.5 .P35 2020 Practical MATLAB deep learning : a project-based approach / | Q325.5 .P48 2017 Elements of causal inference : foundations and learning algorithms / | Q325.5 .R64 2012 A first course in machine learning / | Q325.5 .R64 2020 A first course in machine learning / |
Includes bibliographical references and index
Available to OhioLINK libraries
Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. Youll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. Youll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. 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