Deep Learning is a subfield of Machine Learning because it makes use of Deep Neural Networks inspired by the structure and function of the brain called Artificial Neural Networks.
It is computer software that mimics the network of neurons in a brain.
Deep Learning algorithms are designed with different layers connected to it :
- The first layer is called the Input Layer
- The last layer is called the Output Layer
- All layers in between are called Hidden Layers.
The input layer receives input data. The input layer passes the inputs to the first hidden layer.
The hidden layers perform mathematical computations on our inputs. One of the challenges in creating neural networks is deciding the number of hidden layers, as well as the number of neurons for each layer.
The “Deep” in Deep Learning refers to having more than one hidden layer.
The output layer returns the output data.
The word deep means the network joins neurons in more than two layers. Each hidden layer is composed of neurons. The neurons are connected to each other in the form of layers.
The neurons will process and then propagate the input signal it receives the layer above it. The strength of the signal given the neuron in the next layer depends on the weight, bias, and activation function.
The network consumes large amounts of input data and operates them through multiple layers,the network can learn increasingly complex features of the data at each layer.