A neural network represents a set of algorithms, designed by following the characteristics of the human brain in order to be able to recognize specific patterns.

Moreover, neural networks translate sensory data through some sort of machine perception in order to label or cluster raw input. Such identified patterns are numerical, held in vectors, in which real data such as images or sounds must be translated.

Related Course:
Deep Learning for Computer Vision with Tensor Flow and Keras

Neural Networks

Neural networks are useful in various situations such as clustering and classification. They aid in grouping unlabelled data depending on similarities among the example inputs, while they classify data when they are offered a labelled dataset to train on.

Furthermore, neural networks are able to extract features that are offered to other algorithms for clustering and classification, making deep neural networks part of larger machine-learning applications that involve algorithms like reinforcement learning, classification and regression.

Neural networks is a syntagma used for networks established from a multitude of layers. The layers are made out of nodes, places where computation takes place, similarly to a neuron in a human brain that is able to transmit a signal when enough stimuli is applied.

Moreover, a node gathers input from the data that has a set of coefficients, providing significance to inputs accordingly to the task the algorithm wants to learn. Thus, the information goes through a node`s activation function that determines how that particular signal should develop further in the network.