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Math and architectures of deep learning /

Bibliographic Details
Main Authors:	Chaudhury, Krishnendu (Author), Ashok, Ananya H. (Author), Narumanchi, Sujay (Author), Shankar, Devashish (Author)
Corporate Author:	EBSCOhost
Other Authors:	Banerjee, Prithviraj (writer of foreword.)
Format:	eBook
Language:	English
Published:	Shelter Island, NY : Manning Publications, [2024]
Subjects:	Deep learning (Machine learning) Deep learning (Machine learning) > Mathematics. Python (Computer program language) Apprentissage profond. Apprentissage profond > Mathématiques. Python (Langage de programmation) Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.