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A neural network is an approach to machine learning which is built fundamentally on the principle of how signals are transmitted among the neurons in the human brain. Also known as an artificial neural network, it is a system that learns adaptively by using interconnected neurons or nodes assembled in a layered structured resembling the human brain. The neural network has the ability to learn from data which makes it capable of classifying data, recognizing patterns, and forecasting future events. It breaks down the data input into several layers of abstraction and can be trained using many different examples so that it can accurately recognize the patterns in images, speeches, etc., just like the human brain.
The behavior of a neural network is defined by the way in which the individual elements comprising the network are connected and by the weights or strengths of those connections. The weights are adjusted automatically during the course of training on the basis of specified rules of learning until the neural network starts performing the assigned task as required. Neural networks are among the most suitable and advanced approaches to modeling of non-linear relationships and performing tasks such as recognizing patterns and classifying signals or objects in vision, speech, and control systems.
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Neural networks, especially deep neural networks are being increasingly recognized for their usability and efficiency at tasks that require complex identification such as facial recognition, voice recognition, and text translations. They provide a key approach to technology which drives innovation in various fields of application, from advanced medical research to mobile communication and driver assistance technologies.
Following are the examples where neural networks are used as a primary method of machine learning, demonstrating how important they are for advanced research and technology:
Development of the first neural network dates back to 1958 when Frank Rosenblatt introduced the simple ‘perceptron’ which consisted of a linear regression model and a single neuron having a sigmoid activation function. More complex neural networks have been explored ever since which have led to the development of deep neural networks that contain practically hundreds of layers of neurons that can be seen today.
Note that neural networks containing multiple layers of connected neurons are called deep neural networks whereas those containing just two to three layers are called shallow neural networks.
Some of the commonly used neural networks that can be developed using Matlab for various engineering applications include:
Matlab provides several specialized toolboxes for neural networks, machine learning, deep learning, automated driving assistance, and computer vision. Matlab can be used even by non-experts to develop neural networks simply by writing a few lines of code. It allows users to start on a project quickly, create and visualize models, integrate the models into existing applications, and deploy them to enterprise systems, servers, embedded devices, clouds, and clusters. Developing a neural network in Matlab typically involves the following steps:
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