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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
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**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."
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