Hands-On Machine Learning with C++ Build, Train, and Deploy End-To-end Machine Learning and Deep Learning Pipelines.

This book will help you explore how to implement different well-known machine learning algorithms with various C++ frameworks and libraries. You will cover basic to advanced machine learning concepts with practical and easy to follow examples. By the end of the book, you will be able to build variou...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kolodiazhnyi, Kirill
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2020.
Temas:
Machine learning.
C++ (Computer program language)
Apprentissage automatique.
C++ (Langage de programmation)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Section 1: Overview of Machine Learning
  • Chapter 1: Introduction to Machine Learning with C++
  • Understanding the fundamentals of ML
  • Venturing into the techniques of ML
  • Supervised learning
  • Unsupervised learning
  • Dealing with ML models
  • Model parameter estimation
  • An overview of linear algebra
  • Learning the concepts of linear algebra
  • Basic linear algebra operations
  • Tensor representation in computing
  • Linear algebra API samples
  • Using Eigen
  • Using xtensor
  • Using Shark-ML
  • Using Dlib
  • An overview of linear regression
  • Solving linear regression tasks with different libraries
  • Solving linear regression tasks with Eigen
  • Solving linear regression tasks with Shogun
  • Solving linear regression tasks with Shark-ML
  • Linear regression with Dlib
  • Summary
  • Further reading
  • Chapter 2: Data Processing
  • Technical requirements
  • Parsing data formats to C++ data structures
  • Reading CSV files with the Fast-CPP-CSV-Parser library
  • Preprocessing CSV files
  • Reading CSV files with the Shark-ML library
  • Reading CSV files with the Shogun library
  • Reading CSV files with the Dlib library
  • Reading JSON files with the RapidJSON library
  • Writing and reading HDF5 files with the HighFive library
  • Initializing matrix and tensor objects from C++ data structures
  • Eigen
  • Shark-ML
  • Dlib
  • Shogun
  • Manipulating images with the OpenCV and Dlib libraries
  • Using OpenCV
  • Using Dlib
  • Transforming images into matrix or tensor objects of various libraries
  • Deinterleaving in OpenCV
  • Deinterleaving in Dlib
  • Normalizing data
  • Normalizing with Eigen
  • Normalizing with Shogun
  • Normalizing with Dlib
  • Normalizing with Shark-ML
  • Summary
  • Further reading
  • Chapter 3: Measuring Performance and Selecting Models
  • Technical requirements
  • Performance metrics for ML models
  • Regression metrics
  • Mean squared error and root mean squared error
  • Mean absolute error
  • R squared
  • Adjusted R squared
  • Classification metrics
  • Accuracy
  • Precision and recall
  • F-score
  • AUC-ROC
  • Log-Loss
  • Understanding the bias and variance characteristics
  • Bias
  • Variance
  • Normal training
  • Regularization
  • L1 regularization
  • Lasso
  • L2 regularization - Ridge
  • Data augmentation
  • Early stopping
  • Regularization for neural networks
  • Model selection with the grid search technique
  • Cross-validation
  • K-fold cross-validation
  • Grid search
  • Shogun example
  • Shark-ML example
  • Dlib example
  • Summary
  • Further reading
  • Section 2: Machine Learning Algorithms
  • Chapter 4: Clustering
  • Technical requirements
  • Measuring distance in clustering
  • Euclidean distance
  • Squared Euclidean distance
  • Manhattan distance
  • Chebyshev distance
  • Types of clustering algorithms
  • Partition-based clustering algorithms

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