The fundamental computational unit of a neural network is the **neuron**. A neuron can be visualized as a small processing unit that takes **multiple inputs**, processes them, and gives a **single output**. 

Here's what happens step by step:

1. Each **input** is **multiplied by** a corresponding **weight**. The weights are **learnable parameters** and they determine the importance of the corresponding input.
2. All the weighted inputs are **summed** together.
3. In our implementation, we will also **add** an additional parameter called **bias** to the **input sum**. The bias allows the neuron to **shift** its output up or down, adding **flexibility** to the modeling capability.
4. Then the input sum is passed through an **activation function**. We are using the sigmoid function, which squashes values into the range `(0, 1)`.

> Note
>
> **Bias** of the neuron is also a trainable parameter. 

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

Single Neuron Implementation

Task