Includes bibliographical references (pages 693-715) and index.
Contents:
Part I: Foundational ideas -- An overview of machine learning -- Essential statistics -- Measuring performance -- Bayes' rule -- Curves and surfaces -- Information theory -- Part II: Basic machine learning -- Classification -- Training and testing -- Overfitting and underfitting -- Data preparation -- Classifiers -- Ensembles -- Part III: Deep learning basics -- Neural networks -- Backpropagation -- Optimizers -- Part IV: Beyond the basics -- Convolutional neural networks -- Convnets in practice -- Autoencoders -- Recurrent neural networks -- Attention and transformers -- Reinforcement learning -- Generative adversarial networks -- Creative applications.
Summary:
"A practical, thorough introduction to deep learning, without the usage of advanced math or programming. Covers topics such as image classification, text generation, and the machine learning techniques that are the basis of modern AI"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.