**Perceptron** is the name of the simplest neural network, consisting of only **one hidden layer**. However, in order to be able to solve more complex problems, we will create a variation of perceptron called **multilayer perceptron (MLP)**. A multilayer perceptron consists of **multiple hidden layers**. The structure of a multilayer perceptron looks like this:

1. **An input layer**: It receives the input data;
2. **Hidden layers**: These layers process the data and extract patterns. We have **two hidden layers** in our model;
3. **Output layer**: Produces the final prediction or classifications.

In general, each layer consists of **multiple neurons**, and the **output** from one layer becomes the **input** for the next layer.

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Introduction to Neural Networks

Perceptron Layers

Lösung

Perceptron Layers

Lösung