Deep learning is a branch of machine learning whereby you feed a machine with data and answers, and the machine figures out the rules by which the answers are derived. The answers are the labels for which the data represents for example for data about house prices, the label is the price and the data is the various aspects of a house that affect the price. Another example is image-data about cats and dogs, and the labels are whether an animal is a cat or a dog.
Defining key terminologies
Artificial neural networks, or ANNs, are the building blocks of deep learning. ANNs were first introduced in 1943, and their application has recently taken off due to vast amounts of big data, a massive increase in computing power, and a lot of attention and funding directed towards their progress.
Parts of a neural network
The image below is a basic representation of a neural network.
Neuron — An artificial neuron is a unit or node which has one or more inputs and one output. Each input has an associated weight which can be modified during training. The smiley circles above each represent a neuron.
Layer — This refers to a collection of neurons operating together at a specific depth in a neural network. In the figure above, each of the columns represents a layer and the network has 3 layers; the first layer (input layer) has 3 neurons, the second layer (hidden layer) has 2 neurons, and the third layer (output layer) has 1 neuron.
