Machine Learning Theory to Applications: Theory to Applications

Cover
Title Page
Copyright Page
Preface
Table of Contents
Abbreviations
1. Introduction
2. Linear Algebra
	Matrix rules
	Eigenvalues and eigenvectors
	LU decomposition
	Statistics and probabilities
		Momentums
		Expectation
	Multivariate distributions
		Cauchy distribution
		Dirichlet distribution
		Multimodal distribution
		Student’s t distribution
		Gaussian distribution
3. Machine Learning
	Machine learning approaches
	Historical background
	Data mining
	Optimization
	Statistics
	Theory
	Different kinds of learning algorithms
		Supervised learning
		Unsupervised learning
			K means clustering
			Principal component analysis
		Semi-supervised learning
		Reinforcement learning
		Self-learning
		Feature learning
		Machine learning models
			Linear regression
			Logistic regression
			K nearest neighbor classifier
			Naïve Bayesian classifier
			Artificial neural networks
			Decision trees and random forests
			Support vector machines
			Bayesian networks
			Genetic algorithms
			Federated learning
4. Some Practical Notes
	Resampling method
		Cross validation
		Leave one out cross validation
		K-fold cross validation
	Metrics
		Accuracy
		Precision
		Recall
		F1 score
	Normalization
	Overfitting and underfitting
	Regularization
		Ridge regression
		Lasso regression
		Dropout regularization
	Ceiling analysis
5. Deep Learning
	Overview
	Interpretations
	Artificial neural networks
	Deep neural networks
	Deep learning algorithms
		Convolutional neural networks
		Recurrent neural networks
		Long short term memory networks
		Generative adversarial networks
		Radial basis function networks (RBFNs)
		Multi-layer perceptrons (MLP)
		Deep belief networks
		Restricted Boltzmann machines
		Autoencoders
	Challenges
6. Generative Adversarial Networks
	Generative Adversarial Networks (GANs)
	Conditional GAN (CGAN)
	Auxiliary Classifier GAN (AC-GAN)
	Wasserstein GAN (WGAN)
	WGAN with Gradient Penalty (WGAN-GP)
	Info GAN
	Least Square GAN (LSGAN)
	Bidirectional GAN (BiGAN)
	Dual GAN
	Deep Convolutional GAN (DCGAN)
7. Implementation
	Accelerated computing
	Machine learning frameworks and libraries
		No need for special hardware support
	Interactive data analytic and visualization tools
	Deep learning frameworks and libraries
		TensorFlow
		Keras
		Microsoft CNTK
		Caffe
		Caffe2
		Torch
		PyTorch
		MXNet
		Chainer
		Theano
	Deep learning wrapper libraries
References
Index