Artificial Neural Network(ANN) uses the processing of the brain as a basis to develop algorithms that can be used to model complex patterns and prediction problems. In our brain, there are billions of cells called neurons, which processes information in the form of electric signals.

As we showed, neural networks have many applications such as text classification, information extraction, semantic parsing, question answering, paraphrase detection, language generation, multi-document summarization, machine translation, and speech and character recognition.

The Neural Network architecture is made of individual units called neurons that mimic the biological behavior of the brain. Here are the various components of a neuron. Neuron in Artificial Neural Network. Input – It is the set of features that are fed into the model for the learning process.

Disadvantages of Artificial Neural Networks (ANN)

• Hardware Dependence:
• Unexplained functioning of the network:
• Assurance of proper network structure:
• The difficulty of showing the problem to the network:
• The duration of the network is unknown:

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Neural networks are often the perfect candidate for applications and processes that rely on security, too. For example, a bank processing thousands of credit card transactions may need an automated method of identifying fraudulent transactions.

An Artificial Neural Network is made up of 3 components:

• Input Layer.
• Hidden (computation) Layers.
• Output Layer.

What are the Components of a Neural Network?

• Input. The inputs are simply the measures of our features.
• Weights. Weights represent scalar multiplications.
• Transfer Function. The transfer function is different from the other components in that it takes multiple inputs.
• Activation Function.
• Bias.

Ability to train machine: Artificial neural networks learn events and make decisions by commenting on similar events….

• Hardware dependence: Artificial neural networks require processors with parallel processing power, by their structure.
• Unexplained functioning of the network: This is the most important problem of ANN.

The very most disadvantage of a neural network is its black box nature. Because it has the ability to approximate any function, study its structure but don’t give any insights on the structure of the function being approximated.

Artificial neural networks are computational models that work similarly to the functioning of a human nervous system. There are several kinds of artificial neural networks. These types of networks are implemented based on the mathematical operations and a set of parameters required to determine the output.

Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid.

This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility. Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

There are two approaches to training. Supervised learning provides the network with desired outputs through manual grading of network performance or by delivering desired outputs and inputs. Unsupervised learning occurs when the network makes sense of inputs without outside assistance or instruction.

An introduction to neural networks Kevin Gurney University of Sheffield London and New York © Kevin Gurney 1997 This book is copyright under the Berne Convention. No reproduction without permission. All rights reserved. First published in 1997 by UCL Press UCL Press Limited 11 New Fetter Lane London EC4P 4EE 2

A neural network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the interunit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns.