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Practical MATLAB deep learning : a project-based approach /
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'...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress,
2020.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Paluszek, Michael. | |
| 245 | 1 | 0 | |a Practical MATLAB deep learning : |b a project-based approach / |c Michael Paluszek, Stephanie Thomas. |
| 260 | |a Berkeley, CA : |b Apress, |c 2020. | ||
| 300 | |a 1 online resource (260 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgements -- 1 What Is Deep Learning? -- 1.1 Deep Learning -- 1.2 History of Deep Learning -- 1.3 Neural Nets -- 1.3.1 Daylight Detector -- Problem -- Solution -- How It Works -- 1.3.2 XOR Neural Net -- Problem -- Solution -- How It Works -- 1.4 Deep Learning and Data -- 1.5 Types of Deep Learning -- 1.5.1 Multilayer Neural Network -- 1.5.2 Convolutional Neural Networks (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Networks (LSTMs) -- 1.5.5 Recursive Neural Network | |
| 505 | 8 | |a 1.5.6 Temporal Convolutional Machines (TCMs) -- 1.5.7 Stacked Autoencoders -- 1.5.8 Extreme Learning Machine (ELM) -- 1.5.9 Recursive Deep Learning -- 1.5.10 Generative Deep Learning -- 1.6 Applications of Deep Learning -- 1.7 Organization of the Book -- 2 MATLAB Machine Learning Toolboxes -- 2.1 Commercial MATLAB Software -- 2.1.1 MathWorks Products -- Deep Learning Toolbox -- Instrument Control Toolbox -- Statistics and Machine Learning Toolbox -- Computer Vision System Toolbox -- Image Acquisition Toolbox -- Parallel Computing Toolbox -- Text Analytics Toolbox -- 2.2 MATLAB Open Source | |
| 505 | 8 | |a 2.2.1 Deep Learn Toolbox -- 2.2.2 Deep Neural Network -- 2.2.3 MatConvNet -- 2.2.4 Pattern Recognition and Machine Learning Toolbox (PRMLT) -- 2.3 XOR Example -- 2.4 Training -- 2.5 Zermelo's Problem -- 3 Finding Circles with Deep Learning -- 3.1 Introduction -- 3.2 Structure -- 3.2.1 imageInputLayer -- 3.2.2 convolution2dLayer -- 3.2.3 batchNormalizationLayer -- 3.2.4 reluLayer -- 3.2.5 maxPooling2dLayer -- 3.2.6 fullyConnectedLayer -- 3.2.7 softmaxLayer -- 3.2.8 classificationLayer -- 3.2.9 Structuring the Layers -- 3.3 Generating Data: Ellipses and Circles -- 3.3.1 Problem -- 3.3.2 Solution | |
| 505 | 8 | |a 3.3.3 How It Works -- 3.4 Training and Testing -- 3.4.1 Problem -- 3.4.2 Solution -- 3.4.3 How It Works -- 4 Classifying Movies -- 4.1 Introduction -- 4.2 Generating a Movie Database -- 4.2.1 Problem -- 4.2.2 Solution -- 4.2.3 How It Works -- 4.3 Generating a Movie Watcher Database -- 4.3.1 Problem -- 4.3.2 Solution -- 4.3.3 How It Works -- 4.4 Training and Testing -- 4.4.1 Problem -- 4.4.2 Solution -- 4.4.3 How It Works -- 5 Algorithmic Deep Learning -- 5.1 Building a Detection Filter -- 5.1.1 Problem -- 5.1.2 Solution -- 5.1.3 How It Works -- 5.2 Simulating Fault Detection -- 5.2.1 Problem | |
| 505 | 8 | |a 5.2.2 Solution -- 5.2.3 How It Works -- 5.3 Testing and Training -- 5.3.1 Problem -- 5.3.2 Solution -- 5.3.3 How It Works -- 6 Tokamak Disruption Detection -- 6.1 Introduction -- 6.2 Numerical Model -- 6.2.1 Dynamics -- 6.2.2 Sensors -- 6.2.3 Disturbances -- 6.2.4 Controller -- 6.3 Dynamical Model -- 6.3.1 Problem -- 6.3.2 Solution -- 6.3.3 How It Works -- 6.4 Simulate the Plasma -- 6.4.1 Problem -- 6.4.2 Solution -- 6.4.3 How It Works -- 6.5 Control the Plasma -- 6.5.1 Problem -- 6.5.2 Solution -- 6.5.3 How It Works -- 6.6 Training and Testing -- 6.6.1 Problem -- 6.6.2 Solution | |
| 500 | |a 6.6.3 How It Works | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a MATLAB. |
| 630 | 0 | 7 | |a MATLAB. |2 fast |0 (OCoLC)fst01365096 |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Thomas, Stephanie. | |
| 776 | 0 | 8 | |i Print version: |a Paluszek, Michael. |t Practical MATLAB Deep Learning : A Project-Based Approach. |d Berkeley, CA : Apress L.P., ©2020 |z 9781484251232 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484251249/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 994 | |a 92 |b IZTAP | ||