Deep Neural Networks in a Mathematical Framework

Explore the fascinating world of deep learning with Deep Neural Networks in a Mathematical Framework by Anthony L. L. Caterini. Published by Springer International Publishing AG in 2018, this insightful book spans 84 pages and serves as a comprehensive guide to constructing a rigorous mathematical foundation for deep neural networks.

Delve into the intricacies of gradient descent algorithms presented in a unified manner, applicable to various neural network architectures, including multilayer perceptrons, convolutional neural networks, deep autoencoders, and recurrent neural networks. This first edition is perfect for researchers, students, and professionals looking to deepen their understanding of the mathematical principles underpinning modern AI technologies.

Enhance your knowledge and skills in deep learning with this essential resource that bridges theory and application!