http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (xxvi, 523 pages) : illustrations.
An overview of machine learning and deep learning -- Vectors, matrices, and tensors in machine learning -- Classifiers and vector calculus -- Linear algebraic tools in machine learning -- Probability distributions in machine learning -- Bayesian tools for machine learning -- Function approximation : how neural networks model the world -- Training neural networks : forward propagation and backpropagation -- Loss, optimization, and regularization -- Convolutions in neural networks -- Neural networks for image classification and object detection -- Manifolds, homeomorphism, and neural networks -- Fully Bayes model parameter estimation -- Latent space and generative modeling, autoencoders, and variational autoencoders.
Krishnendu Chaudhury, with Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar ; foreword by Prith Banerjee.