Deep learning (in addition known as deep structured learning) is a type of machine learning technology that uses artificial neural networks to learn interpretations. There are three types of learning. They are as follows
• supervised
• semi-supervised and
• unsupervised
Deep-learning styles such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been used in fields such as computer visualisation, speech recognition, language processing, contraption translation, bioinformatics, drug design, medical image analysis, climate science, material inspection, and board game programmes, producing results that are comparable to, and in some cases superior to, those produced by traditional methods.
In deep learning, the word “deep” refers to the employment of numerous layers in the network.
A linear perceptron cannot be a universal classified, but a network with a nonpolynomial activation function and one hidden layer of unlimited width may, according to early research.
Deep learning is a more recent form that has an infinite number of layers of bounded size, allowing for practical application and optimization while maintaining theoretical universality under moderate circumstances.