Machine Learning Theory to Applications: Theory to Applications

✍ Scribed by Seyedeh Leili Mirtaheri, Reza Shahbazian

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 212
• Edition: 1
• Category: Library

✦ Synopsis

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature, this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars, cognitive decision making, communication networks, security, and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms.

In summary, this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms, covers state-of-the-art, practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications.

✦ Table of Contents

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