Practical MATLAB Deep Learning: A Projects-Based Approach

✍ Scribed by Michael Paluszek, Stephanie Thomas, Eric Ham

• Publisher: Apress
• Year: 2022
• Tongue: English
• Leaves: 338
• Edition: 2
• Category: Library

✦ Synopsis

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  

What You Will Learn

• 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
• Now includes reinforcement learning

Who This Book Is For 
Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.

✦ Table of Contents

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