Welcome to Knowledge City's course on neural networks, introducing neural networks. In these lessons, you'll learn about neural networks and their uses, neural network structure, the single layer perceptron, inputs versus outputs and classification versus regression. In this chapter titled Introducing Neural Networks, what you'll learn in this lesson is, what is a neural network? What do neural networks have to do with machine learning? And what are some general ways in which neural networks are used? So to begin, I'm gonna talk about what is a neural network. A neural network, also referred to as artificial neural network, is a machine designed to model the way that the brain performs a particular task. These tasks are commonly classification or regression. Neural networks are implemented using various algorithms and functions. Now I'm gonna go into how neural networks are related to machine learning. So machine learning, to put it simply, is the process of a machine learning from examples. These machines can be computers, phones, or they can be digital machines such as software programs, et cetera. The use of neural networks is one common method of learning from examples. So what that means is that if machine learning is a process of machine learning from examples, then you can look at neural networks as being one mechanism, or one set of mechanisms, for the machine to learn from these examples. So some common uses of neural networks include function approximation, data processing, learning relationships between data points in large sets of data, developing models that can be used to classify or forecast new and unknown data. So this concludes this lesson and next up I'll be discussing neural network structure.