Course Description
This chapter will introduce neural networks. You’ll learn about what they are and how they are used. You’ll also learn about the most basic type of neural network. Inputs and outputs will be discussed and explained in this chapter. This chapter will also explain the difference between classification and regression for neural networks.
What You'll Learn
- Understand what neural networks are and how they are used
- Explore the structure of a neural network
- Examine the single layer perceptron as the most basic type of neural network
- Distinguish between inputs and outputs in a neural network
- Differentiate between classification and regression for neural networks
Key Takeaways
- Neural networks are introduced in this chapter, covering what they are and how they are used.
- The single layer perceptron is presented as the most basic type of neural network.
- Inputs and outputs of neural networks are discussed and explained.
- The chapter explains the difference between classification and regression for neural networks.
Frequently Asked Questions
What does this course cover?
This chapter introduces neural networks, including what they are and how they are used, the most basic type of neural network, inputs and outputs, and the difference between classification and regression for neural networks.
What are the learning objectives of this course?
The objectives are to understand what neural networks are and to understand the difference between classification and regression.
What lessons are included in this chapter?
The lessons are Neural Networks and Their Uses, Neural Network Structure, Single Layer Perceptron, Inputs vs Outputs, and Classification vs Regression.
What skills does this course relate to?
It relates to Applications Of Artificial Intelligence, Artificial Neural Networks, Backpropagation, Deep Learning, Deep Learning Methods, and Neural Engineering.
