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Neural Networks Practice Sheet
Introduction
Neural networks are a type of Artificial Intelligence (AI) that are modeled after the way the human brain works. They are made up of a network of interconnected nodes (or “neurons”) that process data and can learn from it. Neural networks can be used to solve complex problems, such as recognizing objects in images, understanding language, and predicting outcomes.
Basics
- What is a neural network?
A neural network is a type of Artificial Intelligence (AI) that is modeled after the way the human brain works. It is composed of a network of interconnected nodes (or “neurons”) that process data and can learn from it.
- What are the components of a neural network?
The components of a neural network are the input layer, the hidden layers, and the output layer. The input layer receives data from the outside world, the hidden layers process the data, and the output layer produces the desired result.
- What is the purpose of a neural network?
The purpose of a neural network is to solve complex problems, such as recognizing objects in images, understanding language, and predicting outcomes.
- What is the difference between a neural network and a traditional computer program?
The main difference between a neural network and a traditional computer program is that a neural network can learn from the data it receives. A traditional computer program is limited to the instructions it is given, while a neural network can adjust its parameters to improve its performance.
Practice Problems
- Given the following input data:
Input 1: Red
Input 2: Square
What would be the output of a neural network that is trained to recognize shapes and colors?
Answer: The output of the neural network would be red square
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