Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Learn what is Artificial Neural Networks in detail through this blog.

Capability to mimic the human intelligence by machines is called Artificial Intelligence (AI). Popular approaches towards achieving Artificial Intelligence are if-then formal reasoning, Bayesian inference, probabilistic reasoning and Artificial Neural Networks. Human brain inspired Artificial Neural Networks turned out to be the most effective problem-solving model for wide set of problems of AI.

Key abilities of Artificial Neural Networks:

• Feature extraction: Feature extraction is used in pattern matching and image recognition. It is also called as dimensionality reduction.
• Categorization: It is the process in which the ideas and objects are recognized, understood and interpreted. What a category does is create a relationship between the objects and subjects of knowledge.
• Association: The association rule is used for various applications like uncovering patterns, correlations, in a set of data in order to refine it and make it readily usable.
• Optimization: Optimization is used in many contexts. We can use analytical optimization for designing algorithms or writing proofs. The most important and toughest instance of optimization is with regard to neural network training.
• Generalization: Generalization is the process of deploying the model that is completely trained onto new sets of data that is previously not encountered but gathered from the same distribution that is used to create the model.

What is Artificial Neural Network (ANN)?

Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Fundamental computational units are called nodes. These nodes represents neurons in natural neural networks. These nodes takes an input, perform computation on it and provides an output. Output of one node can be an input for another node. Nodes are connected with each other through edges. Each edge has a weight represented by a real number. Weight is used to guide the direction of execution process. Usually these nodes stay in layers. First layer of artificial neural network is called input layer. Input layer is responsible to take input. Input layer is directly connected with hidden layers. These hidden layers perform computations on input. If a neural network has more than one hidden layers, then it’s called Deep Neural Network. Every node carries a weight. It is a real number. If this value increases it is called strong neural connection. It act as a guiding factor in learning process for the Artificial Neural Network.