Download An Information-Theoretic Approach to Neural Computing by Gustavo Deco, Dragan Obradovic PDF
By Gustavo Deco, Dragan Obradovic
Neural networks offer a robust new expertise to version and keep an eye on nonlinear and intricate platforms. during this booklet, the authors current an in depth formula of neural networks from the information-theoretic point of view. They exhibit how this attitude presents new insights into the layout idea of neural networks. specifically they convey how those tools will be utilized to the themes of supervised and unsupervised studying together with characteristic extraction, linear and non-linear self sufficient part research, and Boltzmann machines. Readers are assumed to have a simple figuring out of neural networks, yet the entire suitable suggestions from details thought are rigorously brought and defined. hence, readers from numerous diverse clinical disciplines, particularly cognitive scientists, engineers, physicists, statisticians, and machine scientists, will locate this to be a truly worthy advent to this topic.
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Extra resources for An Information-Theoretic Approach to Neural Computing
12]. Within a neural network we distinguish between two different types of neurons, namely visible and invisible (or hidden) neurons. The visible neurons process inputs or outputs of the whole neural network and, hence, are divided into the input and output neurons respectively. The hidden neurons define the internal representation of the mapping and have no direct connection to external variables. 2. (a) Feedforward architecture. (b) Recurrent architecture. 3 depicts two principal types of learning paradigms in neural modeling: supervised and unsupervised learning.
E. 2 we will see that there exists an information theoretic based Lyapunov function for this algorithm which tells us the radius of attraction for the obtained solution. o Let us now examine implementations of Plumbley's stochastic approximation method that form some of the common neural learning paradigms.
Principally two types of architecture are defined: feedforward and recurrent. e. there is no backcoupling between neurons. 2 (a). The neurons are arranged in layers. e. all connections are allowed in this case. 2 (b). Recurrent architectures are usually used for the learning of dynamical phenomena since the backcoupling can contain delays. 12]. Within a neural network we distinguish between two different types of neurons, namely visible and invisible (or hidden) neurons. The visible neurons process inputs or outputs of the whole neural network and, hence, are divided into the input and output neurons respectively.