Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

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
			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
Chapter 5: 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
		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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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
Chapter 9: 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
Chapter 10: 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
Chapter 11: 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
Chapter 12: 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
Chapter 13: 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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