If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, ...

In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. This book describes how neural networks operate from the mathematical point of view.

Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way.

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks.

... deep neural networks (DNNs) to estimate a metric structure of the brain that can accommo- date fiber crossings, i.e. ... universal approximation property of fully-connected convolutional networks to approximate the solution ...

A Bayesian and Optimization Perspective Sergios Theodoridis. CHAPTER. NEURAL. NETWORKS. AND. DEEP. LEARNING. 18. CONTENTS. 18.1 18.2 The 18.3 Feed-Forward Multilayer Neural Networks 18.3.1 Fully ... Universal Approximation Property of Feed ...

The world can't wait to see what you can do! Most of all, this book will feed the abstract reasoning region of your mind so that you are able to theorize and invent new types and styles of machine learning. So, what are you waiting for?

Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks ...

Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with ...

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).