Titlebook: Deep Learning Architectures; A Mathematical Appro Ovidiu Calin Textbook 2020 Springer Nature Switzerland AG 2020 neural networks.deep learn

畸形

brother

The Three Threads of ExperienceThis chapter deals with one of the main problems of Deep Learning, namely, . The organization of pixels into features can be assessed by some information measures, such as entropy, conditional entropy, and mutual information. These measures are used to describe the information evolution through the layers of a feedforward network.

Petechiae

MEEK

medium

擴(kuò)音器

Abstract NeuronsThe .? is the building block of any neural network. It is a unit that mimics a biological neuron, consisting of an input (incoming signal), weights (synaptic weights), and activation function (neuron firing model). This chapter introduces the most familiar types of neurons (perceptron, sigmoid neuron, etc.) and investigates their properties.

天賦

Universal ApproximatorsThe answer to the question . is certainly based on the fact that neural networks can approximate well a large family of real-life functions that depend on input variables. The goal of this chapter is to provide mathematical proofs of this behavior for different variants of targets.

molest

農(nóng)學(xué)

冥界三河

Output ManifoldsIn this chapter we shall associate a manifold with each neural network by considering the weights and biasses of a neural network as the coordinate system on the manifold.