A neural network is an artificial intelligence strategy for teaching computers to analyse data in a manner inspired by the human brain. Deep learning is a form of machine learning technique that employs linked nodes or neurons in a layered structure that resembles the human brain.
Artificial neural networks are influenced by actual neurons, where each neuron learns from an event and initiates a signaling process to relay information to other neurons until a conclusion is reached. Artificial neural networks operate by using neurons that acquire knowledge from data points and adjust their connections, including biases and weights, in order to classify data based on its features. An artificial neural network (ANN) consists of neurons that are classified into distinct sections, namely the input layer, hidden layer, and output layer. ANN has same utilization as other classifiers used in the field of machine learning. Nevertheless, in recent years, deep learning methodologies have been used, encompassing the utilization of neural networks consisting of several hundred layers of neurons. These deep learning models have shown higher performance in comparison to many other classification approaches, including Multilayer perceptron.
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