Machine and Deep Learning Using MATLAB
John Wiley & Sons Inc (Verlag)
978-1-394-20908-8 (ISBN)
Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code.
The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues.
Readers will also find information on:
Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning)
Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response)
Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps
Retraining and creation for image labeling, object identification, regression classification, and text recognition
Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.
Kamal I. M. Al-Malah received his PhD degree from Oregon State University in 1993. He served as a Professor of Chemical Engineering in Jordan and Gulf countries, as well as Former Chairman of the Chemical Engineering Department at the University of Hail in Saudi Arabia. Professor Al-Malah is an expert in both Aspen Plus® and MATLAB® applications. He has created a bundle of Windows-based software for engineering applications.
Preface xiii
About the Companion Website xvii
1 Unsupervised Machine Learning (ML) Techniques 1
Introduction 1
Selection of the Right Algorithm in ML 2
Classical Multidimensional Scaling of Predictors Data 2
Principal Component Analysis (PCA) 6
k-Means Clustering 13
Distance Metrics: Locations of Cluster Centroids 13
Replications 14
Gaussian Mixture Model (GMM) Clustering 15
Optimum Number of GMM Clusters 17
Observations and Clusters Visualization 18
Evaluating Cluster Quality 21
Silhouette Plots 22
Hierarchical Clustering 23
Step 1 -- Determine Hierarchical Structure 23
Step 2 -- Divide Hierarchical Tree into Clusters 25
PCA and Clustering: Wine Quality 27
Feature Selection Using Laplacian (fsulaplacian) for Unsupervised Learning 35
CHW 1.1 The Iris Flower Features Data 37
CHW 1.2 The Ionosphere Data Features 38
CHW 1.3 The Small Car Data 39
CHW 1.4 Seeds Features Data 40
2 ML Supervised Learning: Classification Models 42
Fitting Data Using Different Classification Models 42
Customizing a Model 43
Creating Training and Test Datasets 43
Predicting the Response 45
Evaluating the Classification Model 45
KNN Model for All Categorical or All Numeric Data Type 47
KNN Model: Heart Disease Numeric Data 48
Viewing the Fitting Model Properties 50
The Fitting Model: Number of Neighbors and Weighting Factor 51
The Cost Penalty of the Fitting Model 52
KNN Model: Red Wine Data 55
Using MATLAB Classification Learner 57
Binary Decision Tree Model for Multiclass Classification of All Data Types 68
Classification Tree Model: Heart Disease Numeric Data Types 70
Classification Tree Model: Heart Disease All Predictor Data Types 72
Naive Bayes Classification Model for All Data Types 74
Fitting Heart Disease Numeric Data to Naive Bayes Model 75
Fitting Heart Disease All Data Types to Naive Bayes Model 77
Discriminant Analysis (DA) Classifier for Numeric Predictors Only 79
Discriminant Analysis (DA): Heart Disease Numeric Predictors 82
Support Vector Machine (SVM) Classification Model for All Data Types 84
Properties of SVM Model 85
SVM Classification Model: Heart Disease Numeric Data Types 87
SVM Classification Model: Heart Disease All Data Types 90
Multiclass Support Vector Machine (fitcecoc) Model 92
Multiclass Support Vector Machines Model: Red Wine Data 95
Binary Linear Classifier (fitclinear) to High-Dimensional Data 98
CHW 2.1 Mushroom Edibility Data 100
CHW 2.2 1994 Adult Census Income Data 100
CHW 2.3 White Wine Classification 101
CHW 2.4 Cardiac Arrhythmia Data 102
CHW 2.5 Breast Cancer Diagnosis 102
3 Methods of Improving ML Predictive Models 103
Accuracy and Robustness of Predictive Models 103
Evaluating a Model: Cross-Validation 104
Cross-Validation Tune-up Parameters 105
Partition with K-Fold: Heart Disease Data Classification 106
Reducing Predictors: Feature Transformation and Selection 108
Factor Analysis 110
Feature Transformation and Factor Analysis: Heart Disease Data 113
Feature Selection 115
Feature Selection Using predictorImportance Function: Health Disease Data 116
Sequential Feature Selection (SFS): sequentialfs Function with Model Error Handler 118
Accommodating Categorical Data: Creating Dummy Variables 121
Feature Selection with Categorical Heart Disease Data 122
Ensemble Learning 126
Creating Ensembles: Heart Disease Data 130
Ensemble Learning: Wine Quality Classification 131
Improving fitcensemble Predictive Model: Abalone Age Prediction 132
Improving fitctree Predictive Model with Feature Selection (FS): Credit Ratings Data 134
Improving fitctree Predictive Model with Feature Transformation (FT): Credit Ratings Data 135
Using MATLAB Regression Learner 136
Feature Selection and Feature Transformation Using Regression Learner App 145
Feature Selection Using Neighborhood Component Analysis (NCA) for Regression: Big Car Data 146
CHW 3.1 The Ionosphere Data 148
CHW 3.2 Sonar Dataset 149
CHW 3.3 White Wine Classification 150
CHW 3.4 Small Car Data (Regression Case) 152
4 Methods of ML Linear Regression 153
Introduction 153
Linear Regression Models 154
Fitting Linear Regression Models Using fitlm Function 155
How to Organize the Data? 155
Results Visualization: Big Car Data 162
Fitting Linear Regression Models Using fitglm Function 164
Nonparametric Regression Models 166
fitrtree Nonparametric Regression Model: Big Car Data 167
Support Vector Machine, fitrsvm, Nonparametric Regression Model: Big Car Data 170
Nonparametric Regression Model: Gaussian Process Regression (GPR) 172
Regularized Parametric Linear Regression 176
Ridge Linear Regression: The Penalty Term 176
Fitting Ridge Regression Models 177
Predicting Response Using Ridge Regression Models 178
Determining Ridge Regression Parameter, λ 179
The Ridge Regression Model: Big Car Data 179
The Ridge Regression Model with Optimum λ: Big Car Data 181
Regularized Parametric Linear Regression Model: Lasso 183
Stepwise Parametric Linear Regression 186
Fitting Stepwise Linear Regression 187
How to Specify stepwiselm Model? 187
Stepwise Linear Regression Model: Big Car Data 188
CHW 4.1 Boston House Price 192
CHW 4.2 The Forest Fires Data 193
CHW 4.3 The Parkinson’s Disease Telemonitoring Data 194
CHW 4.4 The Car Fuel Economy Data 195
5 Neural Networks 197
Introduction 197
Feed-Forward Neural Networks 198
Feed-Forward Neural Network Classification 199
Feed-Forward Neural Network Regression 200
Numeric Data: Dummy Variables 200
Neural Network Pattern Recognition (nprtool) Application 201
Command-Based Feed-Forward Neural Network Classification: Heart Data 210
Neural Network Regression (nftool) 214
Command-Based Feed-Forward Neural Network Regression: Big Car Data 223
Training the Neural Network Regression Model Using fitrnet Function: Big Car Data 226
Finding the Optimum Regularization Strength for Neural Network Using Cross-Validation: Big Car Data 229
Custom Hyperparameter Optimization in Neural Network Regression: Big Car Data 231
CHW 5.1 Mushroom Edibility Data 233
CHW 5.2 1994 Adult Census Income Data 233
CHW 5.3 Breast Cancer Diagnosis 234
CHW 5.4 Small Car Data (Regression Case) 234
CHW 5.5 Boston House Price 235
6 Pretrained Neural Networks: Transfer Learning 237
Deep Learning: Image Networks 237
Data Stores in MATLAB 241
Image and Augmented Image Datastores 243
Accessing an Image File 246
Retraining: Transfer Learning for Image Recognition 247
Convolutional Neural Network (CNN) Layers: Channels and Activations 256
Convolution 2-D Layer Features via Activations 258
Extraction and Visualization of Activations 261
A 2-D (or 2-D Grouped) Convolutional Layer 264
Features Extraction for Machine Learning 267
Image Features in Pretrained Convolutional Neural Networks (CNNs) 268
Classification with Machine Learning 268
Feature Extraction for Machine Learning: Flowers 269
Pattern Recognition Network Generation 271
Machine Learning Feature Extraction: Spectrograms 275
Network Object Prediction Explainers 278
Occlusion Sensitivity 278
imageLIME Features Explainer 282
gradCAM Features Explainer 284
HCW 6.1 CNN Retraining for Round Worms Alive or Dead Prediction 286
HCW 6.2 CNN Retraining for Food Images Prediction 286
HCW 6.3 CNN Retraining for Merchandise Data Prediction 287
HCW 6.4 CNN Retraining for Musical Instrument Spectrograms Prediction 288
HCW 6.5 CNN Retraining for Fruit/Vegetable Varieties Prediction 289
7 A Convolutional Neural Network (CNN) Architecture and Training 290
A Simple CNN Architecture: The Land Satellite Images 291
Displaying Satellite Images 291
Training Options 294
Mini Batches 295
Learning Rates 296
Gradient Clipping 297
Algorithms 298
Training a CNN for Landcover Dataset 299
Layers and Filters 302
Filters in Convolution Layers 307
Viewing Filters: AlexNet Filters 308
Validation Data 311
Using shuffle Function 316
Improving Network Performance 319
Training Algorithm Options 319
Training Data 319
Architecture 320
Image Augmentation: The Flowers Dataset 322
Directed Acyclic Graphs Networks 329
Deep Network Designer (DND) 333
Semantic Segmentation 342
Analyze Training Data for Semantic Segmentation 343
Create a Semantic Segmentation Network 345
Train and Test the Semantic Segmentation Network 350
HCW 7.1 CNN Creation for Round Worms Alive or Dead Prediction 356
HCW 7.2 CNN Creation for Food Images Prediction 357
HCW 7.3 CNN Creation for Merchandise Data Prediction 358
HCW 7.4 CNN Creation for Musical Instrument Spectrograms Prediction 358
HCW 7.5 CNN Creation for Chest X-ray Prediction 359
HCW 7.6 Semantic Segmentation Network for CamVid Dataset 359
8 Regression Classification: Object Detection 361
Preparing Data for Regression 361
Modification of CNN Architecture from Classification to Regression 361
Root-Mean-Square Error 364
AlexNet-Like CNN for Regression: Hand-Written Synthetic Digit Images 364
A New CNN for Regression: Hand-Written Synthetic Digit Images 370
Deep Network Designer (DND) for Regression 374
Loading Image Data 375
Generating Training Data 375
Creating a Network Architecture 376
Importing Data 378
Training the Network 378
Test Network 383
YOLO Object Detectors 384
Object Detection Using YOLO v4 386
COCO-Based Creation of a Pretrained YOLO v4 Object Detector 387
Fine-Tuning of a Pretrained YOLO v4 Object Detector 389
Evaluating an Object Detector 394
Object Detection Using R-CNN Algorithms 396
R-CNN 397
Fast R-CNN 397
Faster R-CNN 398
Transfer Learning (Re-Training) 399
R-CNN Creation and Training 399
Fast R-CNN Creation and Training 403
Faster R-CNN Creation and Training 408
evaluateDetectionPrecision Function for Precision Metric 413
evaluateDetectionMissRate for Miss Rate Metric 417
HCW 8.1 Testing yolov4ObjectDetector and fasterRCNN Object Detector 424
HCW 8.2 Creation of Two CNN-based yolov4ObjectDetectors 424
HCW 8.3 Creation of GoogleNet-Based Fast R-CNN Object Detector 425
HCW 8.4 Creation of a GoogleNet-Based Faster R-CNN Object Detector 426
HCW 8.5 Calculation of Average Precision and Miss Rate Using GoogleNet-Based Faster R-CNN Object Detector 427
HCW 8.6 Calculation of Average Precision and Miss Rate Using GoogleNet-Based yolov4
Object Detector 427
HCW 8.7 Faster RCNN-based Car Objects Prediction and Calculation of Average Precision for Training and Test Data 427
9 Recurrent Neural Network (RNN) 430
Long Short-Term Memory (LSTM) and BiLSTM Network 430
Train LSTM RNN Network for Sequence Classification 437
Improving LSTM RNN Performance 441
Sequence Length 441
Classifying Categorical Sequences 445
Sequence-to-Sequence Regression Using Deep Learning: Turbo Fan Data 446
Classify Text Data Using Deep Learning: Factory Equipment Failure Text Analysis -- 1 453
Classify Text Data Using Deep Learning: Factory Equipment Failure Text Analysis -- 2 462
Word-by-Word Text Generation Using Deep Learning -- 1 465
Word-by-Word Text Generation Using Deep Learning -- 2 473
Train Network for Time Series Forecasting Using Deep Network Designer (DND) 475
Train Network with Numeric Features 486
HCW 9.1 Text Classification: Factory Equipment Failure Text Analysis 491
HCW 9.2 Text Classification: Sentiment Labeled Sentences Data Set 492
HCW 9.3 Text Classification: Netflix Titles Data Set 492
HCW 9.4 Text Regression: Video Game Titles Data Set 492
HCW 9.5 Multivariate Classification: Mill Data Set 493
HCW 9.6 Word-by-Word Text Generation Using Deep Learning 494
10 Image/Video-Based Apps 495
Image Labeler (IL) App 495
Creating ROI Labels 498
Creating Scene Labels 499
Label Ground Truth 500
Export Labeled Ground Truth 501
Video Labeler (VL) App: Ground Truth Data Creation, Training, and Prediction 502
Ground Truth Labeler (GTL) App 513
Running/Walking Classification with Video Clips using LSTM 520
Experiment Manager (EM) App 526
Image Batch Processor (IBP) App 533
HCW 10.1 Cat Dog Video Labeling, Training, and Prediction -- 1 537
HCW 10.2 Cat Dog Video Labeling, Training, and Prediction -- 2 537
HCW 10.3 EM Hyperparameters of CNN Retraining for Merchandise Data Prediction 538
HCW 10.4 EM Hyperparameters of CNN Retraining for Round Worms Alive or Dead Prediction 539
HCW 10.5 EM Hyperparameters of CNN Retraining for Food Images Prediction 540
Appendix A Useful MATLAB Functions 543
A.1 Data Transfer from an External Source into MATLAB 543
A.2 Data Import Wizard 543
A.3 Table Operations 544
A.4 Table Statistical Analysis 547
A.5 Access to Table Variables (Column Titles) 547
A.6 Merging Tables with Mixed Columns and Rows 547
A.7 Data Plotting 548
A.8 Data Normalization 549
A.9 How to Scale Numeric Data Columns to Vary Between 0 and 1 549
A.10 Random Split of a Matrix into a Training and Test Set 550
A.11 Removal of NaN Values from a Matrix 550
A.12 How to Calculate the Percent of Truly Judged Class Type Cases for a Binary Class Response 550
A.13 Error Function m-file 551
A.14 Conversion of Categorical into Numeric Dummy Matrix 552
A.15 evaluateFit2 Function 553
A.16 showActivationsForChannel Function 554
A.17 upsampLowRes Function 555
A.18A preprocessData function 555
A.18B preprocessData2 function 555
A.19 processTurboFanDataTrain function 556
A.20 processTurboFanDataTest Function 556
A.21 preprocessText Function 557
A.22 documentGenerationDatastore Function 557
A.23 subset Function for an Image Data Store Partition 560
Index 561
| Erscheinungsdatum | 18.10.2023 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 1370 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Naturwissenschaften ► Chemie | |
| Wirtschaft | |
| ISBN-10 | 1-394-20908-8 / 1394209088 |
| ISBN-13 | 978-1-394-20908-8 / 9781394209088 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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