Artificial Neural Networks (ANNs) are computational systems that are inspired by the structure, function and learning mechanism of biological neural networks, such as those found in the brain. ANNs are a subset of machine learning algorithms that are designed to recognize patterns in data by training on large datasets.
How do ANNs work?
Each ANN consists of numerous interconnected nodes called neurons that communicate information with each other. Similar to biological neurons, artificial neurons receive input signals through various dendrites, process the information, and transmit an output signal along an axon to other connected neurons. The strength of these connections, known as weights, is refined by the network during the training process.
Why are ANNs important?
ANNs have a wide range of applications, including image and speech recognition, natural language processing, and financial forecasting. They can learn from large datasets with complex patterns that humans might not be able to discern on their own. ANNs have the ability to learn and improve without being explicitly programmed, making them suitable for a variety of tasks.
Artificial Neural Networks have the potential to revolutionize the field of machine learning and artificial intelligence. With their ability to learn, adapt, and detect patterns, ANNs can provide solutions to complex problems and promote innovation in a variety of fields.