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The function of an artificial neural network (ANN) is to approximate complex functions or patterns by learning from data. ANNs are inspired by the structure and function of the human brain and consist of interconnected nodes, or artificial neurons, organized in layers. These networks are trained using algorithms that adjust the weights and biases of connections between neurons to minimize the difference between the network's output and the desired output, typically through a process called backpropagation. Once trained, an artificial neural network can perform a variety of tasks, including: Pattern Recognition: ANNs can classify inputs into different categories or recognize patterns in data, such as images, text, or speech. Regression: ANNs can learn to predict continuous values based on input data, such as predicting house prices based on features like size, location, and number of bedrooms. Link: https://www.sevenmentor.com/artificial-intelligence-training-courses-in-pune.php Function Approximation: ANNs can approximate complex functions that may be difficult to represent analytically, such as non-linear relationships between variables. Clustering: ANNs can group similar data points together, allowing for unsupervised learning tasks such as clustering. Time Series Analysis: ANNs can analyze sequential data, such as stock prices over time, and make predictions about future values. Control Systems: ANNs can be used to control autonomous systems, such as self-driving cars or robotic arms, by learning to make decisions based on sensory inputs. |