Artificial Neural Networks (ANNs) are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected layers of nodes, known as neurons, that process data and learn patterns through a process called training. Each neuron receives input, processes it with an activation function, and passes the output to the next layer. ANNs are used in various applications such as image recognition, natural language processing, and predictive analytics. By adjusting weights and biases during training, ANNs optimize their ability to make accurate predictions or classifications. They are particularly powerful for tasks involving complex, non-linear relationships in large datasets, making them a cornerstone of deep learning. With advancements in architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), ANNs continue to revolutionize fields like artificial intelligence, healthcare, finance, and robotics.
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