Derive from the beginning a backpropagation (gradient descent) training algorithm for the one-layer RNN with the least squares as an error function. Use the following notation: w =weight vector, x=input vector, h=hidden state y=output unit, ø(x)=activation function, net=output prior to activation function.

Derive from the beginning a backpropagation (gradient descent) training algorithm for the one-layer RNN with the least squares as an error function. Use the following notation: w =weight vector, x=input vector, h=hidden state y=output unit, ø(x)=activation function, net=output prior to activation function.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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