Hello, and welcome to section five on gradient descent. In the last section, we met the perceptron, and the activation functions, and the weights, and all the ingredients that form the simplest neural network. In this section, we will learn about gradient descent and backpropagation. This may sound a bit technical, but I'll make it as intuitive as possible. And it's important that you learn about these things for two reasons. First of all, knowing about the internals of a neural net will allow you to demystify it a little bit. It will cease to be magic, and you will know exactly how it works. You will see that it's actually not that complicated. 

This will make you able to know which problems can be solved with a neural network and which cannot. And so it's important to know about this. The second and important thing is that by knowing how they work internally, you are better able to choose which parameters and which optimizers to tune when you're trying to improve the performance of your model. So at the end of the section, we will build our second neural network model and use it to solve a slightly more complex problem. So let's get started with section five on gradient descent.