Deep learning is a part of machine learning with an algorithm inspired by the structure and function of the brain, which is called an artificial neural network.

Deep learning is suited over a range of fields such as computer vision, speech recognition, natural language processing, etc.

A neural network is called a deep neural network if it contains two or more hidden layers. As the name suggests neural networks are inspired by the brain. Artificial neural networks are computer systems developed with the aim of automatically performing the abilities of the human brain, such as deriving and discovering new information through learning, without any assistance.

Artificial neural networks, inspired by the human brain, imitate biological neural networks and have emerged as a result of mathematical modeling of the learning process. For this reason, studies on this subject first started with the modeling of neurons, which are the biological units that make up the brain, and their application in computer systems, and then it has become used in many fields with the development of computer systems. It is tried to model the learning structure of the human brain with artificial neural networks that can be trained, adaptive and self-organized, learning and evaluating. It is aimed to train, learn and make decisions by means of artificial neural networks, just like humans.

There are three types of layers in the neural network.

1- Input Layer
The input layer contains input neurons that send information to the hidden layer.

2- Hidden Layer
The hidden layer is used to send data to the output layer.

3- Output Layer
The data is made available at the output layer.

But what is a neural network? | Chapter 1, Deep learning
What are the neurons, why are there layers, and what is the math underlying it?