Since our goal is to implement a **multilayer perceptron**, creating a `Perceptron` class will simplify model initialization. Its only **attribute**, `layers` is essentially a list of the `Layer` objects that define the structure of the network:

```python
class Perceptron:
    def __init__(self, layers):
        self.layers = layers
```

The variables used to initialize the layers are the following:

- `input_size`: the number of **input features**;
- `hidden_size`: the number of neurons in **each hidden layer** (both hidden layers will have the same number of neurons in this case);
- `output_size`: the number of neurons in the **output layer**.

The structure of the resulting perceptron should be as follows:

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.

Challenge: Creating a Perceptron

Løsning

Challenge: Creating a Perceptron

Løsning