ANNs

Artificial Neural Networks (ANNs) are a subset of machine learning and are at the heart of deep learning algorithms. Inspired by the human brain, ANNs are composed of interconnected groups of artificial neurons, which process information. Each neuron receives several inputs, performs a weighted sum, adds a bias term, and then applies an activation function to produce an output. This output can then serve as an input to other neurons in the network.

ANNs are typically organized into layers: an input layer, one or more hidden layers, and an output layer. The input layer receives the initial data, while the hidden layers perform computations and feature extraction. The output layer produces the final result. The connections between neurons have associated weights, which are adjusted during training to minimize the difference between the network's predictions and the actual target values.

Training an ANN involves a process called backpropagation, where the network's errors are propagated backward through the network, and the weights are updated using an optimization algorithm like gradient descent. This iterative process continues until the network's performance is satisfactory.

ANNs have been successfully applied to a wide range of tasks, including image and speech recognition, natural language processing, and game playing. They have also been used in various fields such as healthcare, finance, and autonomous vehicles. However, ANNs can be computationally intensive and require large amounts of data for training. Additionally, they can be prone to overfitting, where the network performs well on training data but poorly on unseen data.