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The cost function of a general neural network is defined as J(ŷ,y) 1 m L(VW), y() The loss function L(ỹ(¹), y() is defined by the logistic loss function L(¹),y) = [ylogy) + (1-y)log (1 - ¹)] Please list the stochastic gradient descent update rule, batch gradient descent update rule, and mini-batch gradient descent update rule. Explain the main difference of these three update rules.

The cost function of a general neural network is defined as J(ŷ,y) 1 m L(VW), y() The loss function L(ỹ(¹), y() is defined by the logistic loss function L(¹),y) = [ylogy) + (1-y)log (1 - ¹)] Please list the stochastic gradient descent update rule, batch gradient descent update rule, and mini-batch gradient descent update rule. Explain the main difference of these three update rules.

Database System Concepts
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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The cost function of a general neural network is defined as J(ŷ,y) = m // [4 (9(0), y(1) The loss function L(ŷ), y() is defined by the logistic loss function Ly, y) = [ylogy) + (1-y)log (1 - ¹)] Please list the stochastic gradient descent update rule, batch gradient descent update rule, and mini-batch gradient descent update rule. Explain the main difference of these three update rules.
Transcribed Image Text:The cost function of a general neural network is defined as J(ŷ,y) = m // [4 (9(0), y(1) The loss function L(ŷ), y() is defined by the logistic loss function Ly, y) = [ylogy) + (1-y)log (1 - ¹)] Please list the stochastic gradient descent update rule, batch gradient descent update rule, and mini-batch gradient descent update rule. Explain the main difference of these three update rules.
Given a neural network, its structure is shown below. z." is the output of the linear part of ith neuron in layer l; a¹ = g(z) is the output of the activation part of jth neuron in layer I and g(z) is the activation function. X₁ x₂ 1 Xn [1] [1][1] za z[¹]|a²²] Z3 [1] [¹] a4 XXIS [2][2] z₁ a [2][2] z₂a₂ [2] [3][3] Z₁9₁
Transcribed Image Text:Given a neural network, its structure is shown below. z." is the output of the linear part of ith neuron in layer l; a¹ = g(z) is the output of the activation part of jth neuron in layer I and g(z) is the activation function. X₁ x₂ 1 Xn [1] [1][1] za z[¹]|a²²] Z3 [1] [¹] a4 XXIS [2][2] z₁ a [2][2] z₂a₂ [2] [3][3] Z₁9₁
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