Fill in the blanks with the correct selection from the drop down menu. that is used in most neural network models today. The neuron contains a number of that represent data that defines the problem to be learned; 2. w, which is a set of + that that determines an overall The sigmoid neuron is the main parts: 1. x, which is a set of + that represent how important a piece of data is to the neuron; 3. g, which is a(n) determines an overall value of all the data associated with the neuron; 4. f, which is a(n) output value of the neuron; 5. a(n) which automatically finds the "optimal" values for w.

Fill in the blanks with the correct selection from the drop down menu. that is used in most neural network models today. The neuron contains a number of that represent data that defines the problem to be learned; 2. w, which is a set of + that that determines an overall The sigmoid neuron is the main parts: 1. x, which is a set of + that represent how important a piece of data is to the neuron; 3. g, which is a(n) determines an overall value of all the data associated with the neuron; 4. f, which is a(n) output value of the neuron; 5. a(n) which automatically finds the "optimal" values for w.

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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Concept explainers

Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

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Fill in the blanks with the correct selection from the drop down menu with questions about networks

**Fill in the blanks with the correct selection from the drop-down menu.**

The sigmoid neuron is the **basic computational unit** that is used in most neural network models today. The neuron contains a number of main parts:
1. x, which is a set of **inputs** that represent data that defines the problem to be learned;
2. w, which is a set of **weights** that represent how important a piece of data is to the neuron;
3. g, which is a(n) **activation function** that determines an overall value of all the data associated with the neuron;
4. f, which is a(n) **output function** that determines an overall output value of the neuron;
5. a(n) **learning algorithm** which automatically finds the “optimal” values for w.

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