Deep learning is a subset of Machine learning that mimics the working of the human brain. It is inspired by the human brain cells, called neurons, and works on the concept of neural networks to solve complex real-world problems. It is also known as the deep neural network or deep neural learning.
Some real-world applications of deep learning are :
* Text generation
* Computer vision
* Deep-Learning Robots, etc.
Any Deep neural network will consist of three types of layers :
Input Layer : The input layer has neurons that take the input from external sources like files, data sets, images, videos, and sensors. This part of the Neural Network doesn’t perform any computation. It only transfers the data from the outside world to the Neural Network
Hidden Layer : The hidden layer receives the data from the input layer and uses it to derive results and train several Machine Learning models. The layer can be further divided into sub-layers that extract features, make decisions, connect with other sources, and predict future actions based on the events that happened.
Output layer : After processing, the data is transferred to the output layer for delivering it to the outside environment.
