Practical MATLAB deep learning : a projects-based approach / / Michael Paluszek, Stephanie Thomas, and Eric Ham

• Autore: Paluszek Michael
• Pubblicazione: New York, New York : , : Apress Media LLC, , [2022]; ©2022
• Edizione: Second edition.
• Descrizione fisica: 1 online resource (338 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning
• Persona (resp. second.): HamEric; ThomasStephanie
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Preface to the Second Edition -- 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 Multi-layer Neural Network -- 1.5.2 Convolutional Neural Network (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Network (LSTM) -- 1.5.5 Recursive Neural Network -- 1.5.6 Temporal Convolutional Machine (TCM) -- 1.5.7 Stacked Autoencoders -- 1.5.8 Extreme Learning Machine (ELM) -- 1.5.9 Recursive Deep Learning -- 1.5.10 Generative Deep Learning -- 1.5.11 Reinforcement Learning -- 1.6 Applications of Deep Learning -- 1.7 Organization of the Book -- 2 MATLAB Toolboxes -- 2.1 Commercial MATLAB Software -- 2.1.1 MathWorks Products -- Deep Learning Toolbox -- Instrument Control Toolbox -- Statistics and Machine Learning Toolbox -- Computer Vision Toolbox -- Image Acquisition Toolbox -- Parallel Computing Toolbox -- Text Analytics Toolbox -- 2.2 MATLAB Open Source -- 2.3 XOR Example -- 2.4 Training -- 2.5 Zermelo's Problem -- 3 Finding Circles -- 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 -- 3.3.1 Problem -- 3.3.2 Solution -- 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 Viewer 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 the Filter -- 5.1.1 Problem -- 5.1.2 Solution -- 5.1.3 How It Works -- 5.2 Simulating -- 5.2.1 Problem -- 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 -- 6.6.3 How It Works -- 7 Classifying a Pirouette -- 7.1 Introduction -- 7.1.1 Inertial Measurement Unit -- 7.1.2 Physics -- 7.2 Data Acquisition -- 7.2.1 Problem -- 7.2.2 Solution -- 7.2.3 How It Works -- 7.3 Orientation -- 7.3.1 Problem -- 7.3.2 Solution -- 7.3.3 How It Works -- 7.4 Dancer Simulation -- 7.4.1 Problem -- 7.4.2 Solution -- 7.4.3 How It Works -- 7.5 Real-Time Plotting -- 7.5.1 Problem -- 7.5.2 Solution -- 7.5.3 How It Works -- 7.6 Quaternion Display -- 7.6.1 Problem -- 7.6.2 Solution -- 7.6.3 How It Works -- 7.7 Making the IMU Belt -- 7.7.1 Problem -- 7.7.2 Solution -- 7.7.3 How It Works -- 7.8 Testing the System -- 7.8.1 Problem -- 7.8.2 Solution -- 7.8.3 How It Works -- 7.9 Classifying the Pirouette -- 7.9.1 Problem -- 7.9.2 Solution -- 7.9.3 How It Works -- 7.10 Data Acquisition GUI -- 7.10.1 Problem -- 7.10.2 Solution -- 7.10.3 How It Works -- 7.11 Hardware Sources -- 8 Completing Sentences -- 8.1 Introduction. ; 8.1.1 Sentence Completion -- 8.1.2 Grammar -- 8.1.3 Sentence Completion by Pattern Recognition -- 8.1.4 Sentence Generation -- 8.2 Generating a Database -- 8.2.1 Problem -- 8.2.2 Solution -- 8.2.3 How It Works -- 8.3 Creating a Numeric Dictionary -- 8.3.1 Problem -- 8.3.2 Solution -- 8.3.3 How It Works -- 8.4 Mapping Sentences to Numbers -- 8.4.1 Problem -- 8.4.2 Solution -- 8.4.3 How It Works -- 8.5 Converting the Sentences -- 8.5.1 Problem -- 8.5.2 Solution -- 8.5.3 How It Works -- 8.6 Training and Testing -- 8.6.1 Problem -- 8.6.2 Solution -- 8.6.3 How It Works -- 9 Terrain-Based Navigation -- 9.1 Introduction -- 9.2 Modeling Our Aircraft -- 9.2.1 Problem -- 9.2.2 Solution -- 9.2.3 How It Works -- 9.3 Generating Terrain -- 9.3.1 Problem -- 9.3.2 Solution -- 9.3.3 How It Works -- 9.4 Close-Up Terrain -- 9.4.1 Problem -- 9.4.2 Solution -- 9.4.3 How It Works -- 9.5 Building the Camera Model -- 9.5.1 Problem -- 9.5.2 Solution -- 9.5.3 How It Works -- 9.6 Plotting the Trajectory -- 9.6.1 Problem -- 9.6.2 Solution -- 9.6.3 How It Works -- 9.7 Creating the Training Images -- 9.7.1 Problem -- 9.7.2 Solution -- 9.7.3 How It Works -- 9.8 Training and Testing -- 9.8.1 Problem -- 9.8.2 Solution -- 9.8.3 How It Works -- 9.9 Simulation -- 9.9.1 Problem -- 9.9.2 Solution -- 9.9.3 How It Works -- 10 Stock Prediction -- 10.1 Introduction -- 10.2 Generating a Stock Market -- 10.2.1 Problem -- 10.2.2 Solution -- 10.2.3 How It Works -- 10.3 Creating a Stock Market -- 10.3.1 Problem -- 10.3.2 Solution -- 10.3.3 How It Works -- 10.4 Training and Testing -- 10.4.1 Problem -- 10.4.2 Solution -- 10.4.3 How It Works -- 11 Image Classification -- 11.1 Introduction -- 11.2 Using AlexNet -- 11.2.1 Problem -- 11.2.2 Solution -- 11.2.3 How It Works -- 11.3 Using GoogLeNet -- 11.3.1 Problem -- 11.3.2 Solution -- 11.3.3 How It Works -- 12 Orbit Determination -- 12.1 Introduction. ; 12.2 Generating the Orbits -- 12.2.1 Problem -- 12.2.2 Solution -- 12.2.3 How It Works -- 12.3 Training and Testing -- 12.3.1 Problem -- 12.3.2 Solution -- 12.3.3 How It Works -- 12.4 Implementing an LSTM -- 12.4.1 Problem -- 12.4.2 Solution -- 12.4.3 How It Works -- 13 Earth Sensors -- 13.1 Introduction -- 13.2 Linear Output Earth Sensor -- 13.2.1 Problem -- 13.2.2 Solution -- 13.2.3 How It Works -- 13.3 Segmented Earth Sensor -- 13.3.1 Problem -- 13.3.2 Solution -- 13.3.3 How It Works -- 13.4 Linear Output Sensor Neural Network -- 13.4.1 Problem -- 13.4.2 Solution -- 13.4.3 How It Works -- 13.5 Segmented Sensor Neural Network -- 13.5.1 Problem -- 13.5.2 Solution -- 13.5.3 How It Works -- 14 Generative Modeling of Music -- 14.1 Introduction -- 14.2 Generative Modeling Description -- 14.3 Problem: Music Generation -- 14.4 Solution -- 14.5 Implementation -- 14.6 Alternative Methods -- 15 Reinforcement Learning -- 15.1 Introduction -- 15.2 Titan Lander -- 15.3 Titan Atmosphere -- 15.3.1 Problem -- 15.3.2 Solution -- 15.3.3 How It Works -- 15.4 Simulating the Aircraft -- 15.4.1 Problem -- 15.4.2 Solution -- 15.4.3 How It Works -- 15.5 Simulating Level Flight -- 15.5.1 Problem -- 15.5.2 Solution -- 15.5.3 How It Works -- 15.6 Optimal Trajectory -- 15.6.1 Problem -- 15.6.2 Solution -- 15.6.3 How It Works -- 15.7 Reinforcement Example -- 15.7.1 Problem -- 15.7.2 Solution -- 15.7.3 How It Works -- Bibliography -- Index.
• Sommario/riassunto: 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.
• Titolo autorizzato: Practical MATLAB Deep Learning
• ISBN: 1-4842-7912-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910592983203321
• Lo trovi qui: Univ. Federico II