Hands-On Machine Learning With C++ by Kolodiazhnyi, Kirill -- Read -- Imperial Library of Trantor

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Hands-On Machine Learning with C++

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Preface

Who this book is for What this book covers To get the most out of this book

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Section 1: Overview of Machine Learning 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

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

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 Clustering

Technical requirements Measuring distance in clustering

Euclidean distance Squared Euclidean distance Manhattan distance Chebyshev distance

Types of clustering algorithms

Partition-based clustering algorithms

Distance-based clustering algorithms Graph theory-based clustering algorithms

Spectral clustering algorithms Hierarchical clustering algorithms Density-based clustering algorithms Model-based clustering algorithms

Examples of using the Shogun library for dealing with the clustering task samples

GMM with Shogun K-means clustering with Shogun Hierarchical clustering with Shogun

Examples of using the Shark-ML library for dealing with the clustering task samples

Hierarchical clustering with Shark-ML K-means clustering with Shark-ML

Examples of using the Dlib library for dealing with the clustering task samples

K-means clustering with Dlib Spectral clustering with Dlib Hierarchical clustering with Dlib Newman modularity-based graph clustering algorithm with Dlib Chinese Whispers – graph clustering algorithm with Dlib

Plotting data with C++ Summary Further reading

Anomaly Detection

Technical requirements Exploring the applications of anomaly detection Learning approaches for anomaly detection

Detecting anomalies with statistical tests Detecting anomalies with the Local Outlier Factor method Detecting anomalies with isolation forest Detecting anomalies with One-Class SVM (OCSVM) Density estimation approach (multivariate Gaussian distribution) for anomaly detection

Examples of using different C++ libraries for anomaly detection

C++ implementation of the isolation forest algorithm for anomaly detection Using the Dlib library for anomaly detection

One-Cass SVM with Dlib Multivariate Gaussian model with Dlib

OCSVM with Shogun OCSVM with Shark-ML

Summary Further reading

Dimensionality Reduction

Technical requirements An overview of dimension reduction methods

Feature selection methods Dimensionality reduction methods

Exploring linear methods for dimension reduction

Principal component analysis Singular value decomposition Independent component analysis Linear discriminant analysis Factor analysis Multidimensional scaling

Exploring non-linear methods for dimension reduction

Kernel PCA IsoMap Sammon mapping Distributed stochastic neighbor embedding Autoencoders

Understanding dimension reduction algorithms with various С++ libraries

Using the Dlib library

PCA

Data compression with PCA

LDA Sammon mapping

Using the Shogun library

PCA Kernel PCA MDS IsoMap ICA Factor analysis t-SNE

Using the Shark-ML library

PCA LDA

Summary Further reading

Classification

Technical requirements An overview of classification methods Exploring various classification methods

Logistic regression KRR SVM kNN method Multi-class classification

Examples of using C++ libraries for dealing with the classification task

Using the Shogun library

With logistic regression With SVMs With the kNN algorithm

Using the Dlib library

With KRR With SVM

Using the Shark-ML library

With logistic regression With SVM With the kNN algorithm

Summary Further reading

Recommender Systems

Technical requirements An overview of recommender system algorithms

Non-personalized recommendations Content-based recommendations User-based collaborative filtering Item-based collaborative filtering Factorization algorithms Similarity or preferences correlation

Pearson's correlation coefficient Spearman's correlation Cosine distance

Data scaling and standardization Cold start problem Relevance of recommendations Assessing system quality

Understanding collaborative filtering method details Examples of item-based collaborative filtering with C++

Using the Eigen library Using the mlpack library

Summary Further reading

Ensemble Learning

Technical requirements An overview of ensemble learning

Using a bagging approach for creating ensembles Using a gradient boosting method for creating ensembles Using a stacking approach for creating ensembles Using the random forest method for creating ensembles

Decision tree algorithm overview Random forest method overview

Examples of using C++ libraries for creating ensembles

Ensembles with Shogun

Using gradient boosting with Shogun Using random forest with Shogun

Ensembles with Shark-ML

Using random forest with Shark-ML Using a stacking ensemble with Shark-ML

Summary Further reading

Section 3: Advanced Examples Neural Networks for Image Classification

Technical requirements An overview of neural networks

Neurons The perceptron and neural networks Training with the backpropagation method

Backpropagation method modes

Stochastic mode Batch mode Mini-batch mode

Backpropagation method problems The backpropagation method – an example

Loss functions Activation functions

The stepwise activation function The linear activation function The sigmoid activation function The hyperbolic tangent Activation function properties

Regularization in neural networks

Different methods for regularization

Neural network initialization

Xavier initialization method He initialization method

Delving into convolutional networks

Convolution operator Pooling operation Receptive field Convolution network architecture

What is deep learning? Examples of using C++ libraries to create neural networks

Simple network example for the regression task

Dlib Shogun Shark-ML

Architecture definition Loss function definition Network initialization Optimizer configuration Network training The complete programming sample

Understanding image classification using the LeNet architecture

Reading the training dataset

Reading dataset files Reading the image file

Neural network definition Network training

Summary Further reading

Sentiment Analysis with Recurrent Neural Networks

Technical requirements An overview of the RNN concept Training RNNs using the concept of backpropagation through time Exploring RNN architectures

LSTM GRUs Bidirectional RNN Multilayer RNN

Understanding natural language processing with RNNs

Word2Vec GloVe

Sentiment analysis example with an RNN Summary Further reading

Section 4: Production and Deployment Challenges Exporting and Importing Models

Technical requirements ML model serialization APIs in C++ libraries

Model serialization with Dlib Model serialization with Shogun Model serialization with Shark-ML Model serialization with PyTorch

Neural network initialization Using the torch::save and torch::load functions Using PyTorch archive objects

Delving into ONNX format

Loading images into Caffe2 tensors Reading the class definition file

Summary Further reading

Deploying Models on Mobile and Cloud Platforms

Technical requirements Image classification on Android mobile

The mobile version of the PyTorch framework Using TorchScript for a model snapshot The Android Studio project

The UI and Java part of the project The C++ native part of the project

Machine learning in the cloud – using Google Compute Engine

The server The client Service deployment

Summary Further reading

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