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Hands-On Machine Learning with C++ - Second Edition

Name: Hands-On Machine Learning with C++ - Second Edition
Author: Kirill Kolodiazhnyi
ISBN: 9781805120575

by Kirill Kolodiazhnyi

January 2025

Intermediate to advanced

518 pages

14h 51m

English

Packt Publishing

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Hands-On Machine Learning with C++
Contributors
About the author
About the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsFree Benefits with Your Book
Part 1:Overview of Machine Learning
Chapter 1: Introduction to Machine Learning with C++
Understanding the fundamentals of MLVenturing into the techniques of MLDealing with ML modelsModel parameter estimationAn overview of linear algebraLearning the concepts of linear algebraBasic linear algebra operationsTensor representation in computingLinear algebra API samplesAn overview of linear regressionSolving linear regression tasks with different librariesSolving linear regression tasks with EigenSolving linear regression tasks with BlazeSolving linear regression tasks with ArrayFireLinear regression with DlibSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 2: Data Processing
Technical requirementsParsing data formats to C++ data structuresReading CSV files with the Fast-CPP-CSV-Parser libraryPreprocessing CSV filesReading CSV files with the mlpack libraryReading CSV files with the Dlib libraryReading JSON files with the nlohmann-json libraryWriting and reading HDF5 files with the HighFive libraryInitializing matrix and tensor objects from C++ data structuresWorking with the Eigen libraryWorking with the Blaze libraryWorking with the Dlib libraryWorking with the ArrayFire libraryWorking with the mlpack libraryManipulating images with the OpenCV and Dlib librariesUsing OpenCVUsing DlibTransforming images into matrix or tensor objects of various librariesDeinterleaving in OpenCVDeinterleaving in DlibNormalizing dataNormalizing with EigenNormalizing with mlpackNormalizing with DlibNormalizing with FlashlightSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 3: Measuring Performance and Selecting Models
Technical requirementsPerformance metrics for ML modelsRegression metricsClassification metricsUnderstanding bias and variance characteristicsBiasVarianceNormal trainingRegularizationModel selection with the grid search techniqueCross-validationGrid searchmlpack exampleOptuna with Flashlight exampleDlib exampleSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Part 2: Machine Learning Algorithms
Chapter 4: Clustering
Technical requirementsMeasuring distance in clusteringEuclidean distanceSquared Euclidean distanceManhattan distanceChebyshev distanceTypes of clustering algorithmsPartition-based clustering algorithmsDistance-based clustering algorithmsGraph theory-based clustering algorithmsSpectral clustering algorithmsHierarchical clustering algorithmsDensity-based clustering algorithmsModel-based clustering algorithmsExamples of using the mlpack library for dealing with the clustering task samplesGMM and EM with mlpackK-means clustering with mlpackDBSCAN with mlpackMeanShift clustering with mlpackExamples of using the Dlib library for dealing with the clustering task samplesK-means clustering with DlibSpectral clustering with DlibHierarchical clustering with DlibNewman modularity-based graph clustering algorithm with DlibChinese Whispers – graph clustering algorithm with DlibPlotting data with C++SummaryFurther readingGet This Book's PDF Version and Exclusive Extras

Chapter 5: Anomaly Detection
Technical requirementsExploring the applications of anomaly detectionLearning approaches for anomaly detectionDetecting anomalies with statistical testsDetecting anomalies with the Local Outlier Factor methodDetecting anomalies with isolation forestDetecting anomalies with One-Class Support Vector MachineDensity estimation approachUsing multivariate Gaussian distribution for anomaly detectionExamples of using different C++ libraries for anomaly detectionC++ implementation of the isolation forest algorithm for anomaly detectionUsing the Dlib library for anomaly detectionSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 6: Dimensionality Reduction
Technical requirementsAn overview of dimension-reduction methodsFeature selection methodsDimensionality reduction methodsExploring linear methods for dimension-reductionPrincipal component analysisSingular value decompositionIndependent component analysisLinear discriminant analysisFactor analysisMultidimensional scalingExploring non-linear methods for dimension-reductionKernel PCAIsomapSammon mappingDistributed stochastic neighbor embeddingAutoencodersUnderstanding dimension-reduction algorithms with various С++ librariesUsing the Dlib libraryUsing the Tapkee librarySummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 7: Classification
Technical requirementsAn overview of classification methodsExploring various classification methodsLogistic regressionKRRSVMkNN methodMulti-class classificationExamples of using C++ libraries for dealing with the classification taskUsing the mlpack libraryUsing the Dlib libraryUsing the Flashlight librarySummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 8: Recommender Systems
Technical requirementsAn overview of recommender system algorithmsNon-personalized recommendationsContent-based recommendationsUser-based collaborative filteringItem-based collaborative filteringFactorization algorithmsSimilarity or preferences correlationData scaling and standardizationCold start problemRelevance of recommendationsAssessing system qualityUnderstanding the collaborative filtering methodExamples of item-based collaborative filtering with C++Using the Eigen libraryUsing the mlpack librarySummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 9: Ensemble Learning
Technical requirementsAn overview of ensemble learningUsing a bagging approach for creating ensemblesUsing a gradient boosting method for creating ensemblesUsing a stacking approach for creating ensemblesUsing the random forest method for creating ensemblesExamples of using C++ libraries for creating ensemblesEnsembles with DlibEnsembles with mlpackSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Part 3: Advanced Examples
Chapter 10: Neural Networks for Image Classification
Technical requirementsAn overview of neural networksNeuronsThe perceptron and neural networksTraining with the backpropagation methodLoss functionsActivation functionsRegularization in neural networksNeural network initializationDelving into convolutional networksConvolution operatorPooling operationReceptive fieldConvolution network architectureWhat is deep learning?Examples of using C++ libraries to create neural networksDlibmlpackFlashlightUnderstanding image classification using the LeNet architectureReading the training datasetNeural network definitionNetwork trainingSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 11: Sentiment Analysis with BERT and Transfer Learning
Technical requirementsAn overview of the Transformer architectureEncoderDecoderTokenizationWord embeddingsUsing the encoder and decoder parts separatelySentiment analysis example with BERTExporting the model and vocabularyImplementing the tokenizerImplementing the dataset loaderImplementing the modelTraining the modelSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Part 4: Production and Deployment Challenges
Chapter 12: Exporting and Importing Models
Technical requirementsML model serialization APIs in C++ librariesModel serialization with DlibModel serialization with FlashlightModel serialization with mlpackModel serialization with PyTorchDelving into the ONNX formatUsing the ResNet architecture for image classificationLoading images into onnxruntime tensorsReading the class definition fileSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 13: Tracking and Visualizing ML Experiments
Technical requirementsUnderstanding visualization and tracking systems for experimentsTensorBoardMLflowExperiment tracking with MLflow’s REST APIImplementing MLflow’s REST C++ clientLogging metric values and running parametersIntegrating experiment tracking into linear regression trainingExperiments tracking processSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 14: Deploying Models on a Mobile Platform
Technical requirementsDeveloping object detection on AndroidThe mobile version of the PyTorch frameworkUsing TorchScript for a model snapshotThe Android Studio projectThe Kotlin part of the projectThe native C++ part of the projectSummaryFurther readingGet This Book's PDF Version and Exclusive Extras
Chapter 15: Unlock Your Exclusive Benefits
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Content preview from Hands-On Machine Learning with C++ - Second Edition

8 Recommender Systems

Recommender systems are algorithms, programs, and services that are designed to use data to predict which objects (goods or services) are of interest to a user. There are two main types of recommender systems: content-based and collaborative filtering. Content-based recommender systems are based on data that’s been collected from specific products. They recommend objects to a user that are similar to ones the user has previously acquired or shown interest in. Collaborative filtering recommender systems filter out objects that a user might like based on the reaction history of other, similar users of these systems. They also usually consider the user’s previous reactions.

In this chapter, we’ll learn how to implement recommender ...

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Hands-On Machine Learning with C++ - Second Edition

by Kirill Kolodiazhnyi

8

Recommender Systems

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.