About Artificial Neural Network (ANN):

Definition: Artificial Neural Networks (ANN) also known as Neural Networks are a vital subset of machine learning and are at the heart of deep learning algorithms.

• Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
• They are not like other machine learning algorithms that process numbers or organize data, it is an algorithm that learns from experience and repeated tasks performed by users.
• It is fed massive volumes of data in the beginning phases. In most cases, training is done by providing input and informing the network about what should be the output.

Note:

Backpropagation is a commonly used method for training artificial neural networks, especially deep neural networks.

Applications of Artificial Neural Network (ANN):

Image Preprocessing and Character Recognition, Forecasting, Credit rating, Fraud Detection, Portfolio Management among others.

Limitations of Artificial Neural Network (ANN)

Time it takes to train networks, which frequently demand an acceptable level of computational power for even complex tasks.

• Neural networks are computer systems in which the user categorizes the trained data and gets responses. They have the ability to fine-tune the responses, but they do not have access to the specific decision-making process.

Source:  Robotics, AI, and others