Homework Help is Here – Start Your Trial Now!
Engineering
Computer Science
Which statement about ANNs is false? The back-propagation algorithm uses the gradient descent method Hidden layers allow the network to extract higher-order statistics from its input The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer.

Which statement about ANNs is false? The back-propagation algorithm uses the gradient descent method Hidden layers allow the network to extract higher-order statistics from its input The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer.

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
See similar textbooks
icon
Related questions
Question

Its not D

**Quiz Question: Understanding Artificial Neural Networks (ANNs)** **Question:** Which statement about ANNs is false? **Options:** - ○ The back-propagation algorithm uses the gradient descent method - ○ Hidden layers allow the network to extract higher-order statistics from its input - ○ The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer - ● Complex network topologies carry the risk of overfitting **Explanation of Options:** 1. **The back-propagation algorithm uses the gradient descent method:** - Back-propagation is a widely-used algorithm in training neural networks and typically utilizes gradient descent to minimize the error (loss) by adjusting the weights. 2. **Hidden layers allow the network to extract higher-order statistics from its input:** - Hidden layers in ANNs provide the ability to model complex relationships by transforming inputs through layers of neurons, allowing the network to capture intricate data patterns. 3. **The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer:** - In a convolutional layer, neurons are connected only to a local region of the input, rather than being fully connected. This allows for efficient spatial hierarchy learning (note this statement is false). 4. **Complex network topologies carry the risk of overfitting:** - Overfitting occurs when a model learns the training data too well, including its noise and outliers. Complex architectures can increase this risk if not properly managed with techniques such as regularization, dropout, and validation. This statement is true. **Note:** The statement marked as the false option in the context is: "The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer."
Transcribed Image Text:**Quiz Question: Understanding Artificial Neural Networks (ANNs)** **Question:** Which statement about ANNs is false? **Options:** - ○ The back-propagation algorithm uses the gradient descent method - ○ Hidden layers allow the network to extract higher-order statistics from its input - ○ The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer - ● Complex network topologies carry the risk of overfitting **Explanation of Options:** 1. **The back-propagation algorithm uses the gradient descent method:** - Back-propagation is a widely-used algorithm in training neural networks and typically utilizes gradient descent to minimize the error (loss) by adjusting the weights. 2. **Hidden layers allow the network to extract higher-order statistics from its input:** - Hidden layers in ANNs provide the ability to model complex relationships by transforming inputs through layers of neurons, allowing the network to capture intricate data patterns. 3. **The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer:** - In a convolutional layer, neurons are connected only to a local region of the input, rather than being fully connected. This allows for efficient spatial hierarchy learning (note this statement is false). 4. **Complex network topologies carry the risk of overfitting:** - Overfitting occurs when a model learns the training data too well, including its noise and outliers. Complex architectures can increase this risk if not properly managed with techniques such as regularization, dropout, and validation. This statement is true. **Note:** The statement marked as the false option in the context is: "The neurons in a convolutional layer are fully connected to the neurons/pixels in the previous layer."
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Knowledge Booster
Fundamentals of Blockchaining
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.
Similar questions
  • SEE MORE QUESTIONS
Recommended textbooks for you
Database System Concepts
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
C How to Program (8th Edition)
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
Database Systems: Design, Implementation, & Manag…
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
Programmable Logic Controllers
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education
SEE MORE TEXTBOOKS