An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain. It consists of a network of interconnected nodes, called artificial neurons, that process input information and generate an output.
In an ANN, each artificial neuron receives one or more inputs and processes them using an activation function, which determines the neuron's output. The output of each neuron is transmitted to other neurons through weighted connections, which are used to adjust the contribution of each neuron to the final output.
During the training of an ANN, the model adjusts the weights of the connections between neurons to minimize the difference between the desired output and the actual output. This process is carried out using optimization algorithms, such as gradient descent.
ANNs are used in a wide variety of machine learning applications, such as image classification, natural language processing and time series prediction. They have also been used in complex problem solving, such as deep machine learning and the creation of recurrent and convolutional neural networks.
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