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Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Next, you'll learn to model a quadcopter example project in depth. Along the way, you’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll apply deep learning for quadcopter control, sensing, and navigation via simulations. Finally, you'll experience how to fly a hardware quadcopter and then use it for deep-learning experiments to motivate future projects and innovations. What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and demonstrate with examples Use MATLAB toolboxes related to deep learning Employ a quadcopter case study throughout book Build the quadcopter Discover the prerequisites for using deep learning with our quadcopter Create quadcopter control, sensing, and navigation via simulations Fly a real hardware quadcopter; use it to carry out additional deep-learning experiments Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.
Acquisto del libro
Practical MATLAB Deep Learning, Stephanie Thomas, Michael Paluszek
- Lingua
- Pubblicato
- 2020
- product-detail.submit-box.info.binding
- (In brossura)
Metodi di pagamento
Qui potrebbe esserci la tua recensione.
- Titolo
- Practical MATLAB Deep Learning
- Lingua
- Inglese
- Autori
- Stephanie Thomas, Michael Paluszek
- Editore
- Apress
- Pubblicato
- 2020
- Formato
- In brossura
- Pagine
- 268
- ISBN10
- 1484251237
- ISBN13
- 9781484251232
- Serie
- Valutazione
- 2,5 su 5
- Descrizione
- Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Next, you'll learn to model a quadcopter example project in depth. Along the way, you’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll apply deep learning for quadcopter control, sensing, and navigation via simulations. Finally, you'll experience how to fly a hardware quadcopter and then use it for deep-learning experiments to motivate future projects and innovations. What You Will Learn Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and demonstrate with examples Use MATLAB toolboxes related to deep learning Employ a quadcopter case study throughout book Build the quadcopter Discover the prerequisites for using deep learning with our quadcopter Create quadcopter control, sensing, and navigation via simulations Fly a real hardware quadcopter; use it to carry out additional deep-learning experiments Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.