Название: Machine Learning with Python: Theory and ApplicationsАвтор: G. R. Liu
Издательство: World Scientific Publishing
Год: 2023
Страниц: 693
Язык: английский
Формат: pdf (true)
Размер: 100.0 MB
Machine Learning (ML) has become a very important area of research widely used in various industries.
This compendium introduces the basic concepts, fundamental theories and essential computational techniques related to ML models. With most essential basics and a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.
This book will cover most of these algorithms (Linear and logistic regression, Decision Tree, Support Vector Machine, Naive Bayes, etc.), but our focus will be more on neural network-based models because rigorous theory and predictive models can be established. Machine Learning is a very active area of research and development. New models, including the so-called cognitive machine learning models, are being studied.
Different types of effective artificial Neural Networks (NNs) with various configurations have been developed and widely used for practical problems in sciences and engineering, including multilayer perceptron (MLP), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). TrumpetNets and TubeNets were also recently proposed by the author for creating two-way deepnets using physics-law-based models as trainers, such as the FEM and S-FEM.
Machine Learning is essentially to mimic the natural learning process occurring in biological brains that can have a huge number of neurons. In terms of usage of data, we may have three major categories:
1. Supervised Learning, using data with true labels (teachers).
2. Unsupervised Learning, using data without labels.
3. Reinforcement Learning, using a predefined environment.
The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks.
Contents:
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