An Artificial Neural Network (ANN) is a computational model based on the way biological neural networks in the human brain process information. ANNs are used in artificial intelligence (AI) to recognize patterns, learn from data and make decisions. They consist of layers of interconnected nodes or neurons, where each connection has a weight that adjusts during the learning process.
ANNs usually have an input layer, one or more hidden layers and an output layer. The input layer receives the initial data, which is then passed through the hidden layers where the actual processing and learning takes place. The output layer delivers the final result. The neurons in each layer are connected to the neurons in the subsequent layer and these connections carry weights that are adjusted during the learning process.
The learning process in Artificial Neural Network involves adjusting the weights of these connections based on the error of the output compared to the expected result. This is often done using a method called backpropagation, which calculates the error contribution of each neuron and adjusts the weights to minimize the overall error.
ANNs are particularly powerful in tasks such as image and speech recognition, where they can recognize patterns and make decisions based on complex, high-dimensional data. They are also used in applications such as language translation, medical diagnosis and even playing complex games such as chess and Go.
Despite their capabilities, ANNs have some challenges to overcome. They require large amounts of data and computing power to train effectively. They can also be considered a “black box” as it is often difficult to understand how they arrive at their decisions. This lack of transparency can be a problem in critical applications where understanding the decision-making process is important.
