MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results. 2 ed

Cover....1 Title Page....2 Copyright and Credits....2 Contributors....4 Table of Contents....8 Preface....14 Part 1: Getting Started with Matlab....22 Chapter 1: Exploring MATLAB for Machine Learning....24 Technical requirements....24 Introducing ML....25 How to define ML....25 Analysis of logical reasoning....25 Learning strategy typologies....27 Discovering the different types of learning processes....29 Supervised learning....30 Unsupervised learning....31 Reinforcement learning....32 Semi-supervised learning....33 Transfer learning....34 Using ML techniques....34 Selecting the ML paradigm....35 Step-by-step guide on how to build ML models....37 Exploring MATLAB toolboxes for ML....40 Statistics and Machine Learning Toolbox....41 Deep Learning Toolbox....42 Reinforcement Learning Toolbox....43 Computer Vision Toolbox....44 Text Analytics Toolbox....45 ML applications in real life....46 Summary....47 Chapter 2: Working with Data in MATLAB....50 Technical requirements....51 Importing data into MATLAB....51 Exploring the Import Tool....53 Using the load() function to import files....55 Reading ASCII-delimited files....57 Exporting data from MATLAB....60 Working with different types of data....63 Working with images....63 Audio data handling....64 Exploring data wrangling....65 Introducing data cleaning....66 Discovering exploratory statistics....68 EDA....69 EDA in practice....69 Introducing exploratory visualization....72 Understanding advanced data preprocessing techniques in MATLAB....73 Data normalization for feature scaling....74 Introducing correlation analysis in MATLAB....75 Summary....79 Part 2: Understanding Machine Learning Algorithms in MATLAB....82 Chapter 3: Prediction Using Classification and Regression....84 Technical requirements....84 Introducing classification methods using MATLAB....85 Decision trees for decision-making....85 Exploring decision trees in MATLAB....87 Building an effective and accurate classifier....93 SVMs explained....93 Supervised classification using SVM....96 Exploring different types of regression....99 Introducing linear regression....99 Linear regression model in MATLAB....100 Making predictions with regression analysis in MATLAB....103 Multiple linear regression with categorical predictor....103 Evaluating model performance....107 Reducing outlier effects....108 Using advanced techniques for model evaluation and selection in MATLAB....111 Understanding k-fold cross-validation....112 Exploring leave-one-out cross-validation....115 Introducing the bootstrap method....116 Summary....116 Chapter 4: Clustering Analysis and Dimensionality Reduction....118 Technical requirements....118 Understanding clustering – basic concepts and methods....119 How to measure similarity....119 How to find centroids and centers....121 How to define a grouping....122 Understanding hierarchical clustering....123 Partitioning-based clustering algorithms with MATLAB....129 Introducing the k-means algorithm....129 Using k-means in MATLAB....130 Grouping data using the similarity measures....136 Applying k-medoids in MATLAB....137 Discovering dimensionality reduction techniques....140 Introducing feature selection methods....141 Exploring feature extraction algorithms....143 Feature selection and feature extraction using MATLAB....145 Stepwise regression for feature selection....145 Carrying out PCA....150 Summary....156 Chapter 5: Introducing Artificial Neural Network Modeling....158 Technical requirements....158 Getting started with ANNs....159 Basic concepts relating to ANNs....159 Understanding how perceptrons work....161 Activation function to introduce non-linearity....162 ANN’s architecture explained....164 Training and testing an ANN model in MATLAB....165 How to train an ANN....165 Introducing the MATLAB Neural Network Toolbox....166 Understanding data fitting with ANNs....169 Discovering pattern recognition using ANNs....178 Building a clustering application with an ANN....187 Exploring advanced optimization techniques....192 Understanding SGD....193 Exploring Adam optimization....195 Introducing second-order methods....196 Summary....198 Chapter 6: Deep Learning and Convolutional Neural Networks....200 Technical requirements....201 Understanding DL basic concepts....201 Automated feature extraction....201 Training a DNN....202 Exploring DL models....203 Approaching CNNs....205 Convolutional layer....206 Pooling layer....208 ReLUs....209 FC layer....209 Building a CNN in MATLAB....210 Exploring the model’s results....218 Discovering DL architectures....222 Understanding RNNs....222 Analyzing LSTM networks....223 Introducing transformer models....225 Summary....225 Part 3: Machine Learning in Practice....228 Chapter 7: Natural Language Processing Using MATLAB....230 Technical requirements....230 Explaining NLP....231 NLA....233 NLG....234 Analyzing NLP tasks....235 Introducing automatic processing....237 Exploring corpora and word and sentence tokenizers....237 Corpora....238 Words....239 Sentence tokenize....240 Implementing a MATLAB model to label sentences....240 Introducing sentiment analysis....241 Movie review sentiment analysis....242 Using an LSTM model for label sentences....243 Understanding gradient boosting techniques....249 Approaching ensemble learning....249 Bagging definition and meaning....250 Discovering random forest....251 Boosting algorithms explained....252 Summary....254 Chapter 8: MATLAB for Image Processing and Computer Vision....256 Technical requirements....256 Introducing image processing and computer vision....257 Understanding image processing....257 Explaining computer vision....261 Exploring MATLAB tools for computer vision....262 Building a MATLAB model for object recognition....264 Introducing handwriting recognition (HWR)....265 Training and fine-tuning pretrained deep learning models in MATLAB....271 Introducing the ResNet pretrained network....272 The MATLAB Deep Network Designer app....273 Interpreting and explaining machine learning models....278 Understanding saliency maps....278 Understanding feature importance scores....279 Discovering gradient-based attribution methods....280 Summary....281 Chapter 9: Time Series Analysis and Forecasting with MATLAB....282 Technical requirements....282 Exploring the basic concepts of time series data....283 Understanding predictive forecasting....283 Introducing forecasting methodologies....284 Time series analysis....286 Extracting statistics from sequential data....290 Converting a dataset into a time series format in MATLAB....291 Understanding time series slicing....293 Resampling time series data in MATLAB....294 Moving average....295 Exponential smoothing....297 Implementing a model to predict the stock market....299 Dealing with imbalanced datasets in MATLAB....309 Understanding oversampling....310 Exploring undersampling....311 Summary....312 Chapter 10: MATLAB Tools for Recommender Systems....314 Technical requirements....314 Introducing the basic concepts of recommender systems....315 Understanding CF....315 Content-based filtering explained....316 Hybrid recommender systems....317 Finding similar users in data....318 Creating recommender systems for network intrusion detection using MATLAB....324 Recommender system for NIDS....325 NIDS using a recommender system in MATLAB....326 Deploying machine learning models....330 Understanding model compression....331 Discovering model pruning techniques....331 Introducing quantization for efficient inference on edge devices....333 Getting started with knowledge distillation....333 Learning low-rank approximation....334 Summary....335 Chapter 11: Anomaly Detection in MATLAB....336 Technical requirements....336 Introducing anomaly detection and fault diagnosis systems....337 Anomaly detection overview....337 Fault diagnosis systems explained....338 Approaching fault diagnosis using ML....340 Using ML to identify anomalous functioning....342 Anomaly detection using logistic regression....343 Improving accuracy using the Random Forest algorithm....347 Building a fault diagnosis system using MATLAB....349 Understanding advanced regularization techniques....353 Understanding dropout....353 Exploring L1 and L2 regularization....354 Introducing early stopping....355 Summary....356 Index....358 Other Books You May Enjoy....1