In deep learning, a subfield within the broader field of machine learning, the goal is still to learn a function but by employing an architecture that mimics the neural architecture found in the human brain in order to learn from experience using a hierarchy of concepts or representations. This enables us to develop more complex and powerful functions in order to predict outcomes better.
Many machine learning models employ a two-layer architecture, where some sort of function maps an input to an output. However, in the human brain, multiple layers of processing are found, in other words, a neural network. By mimicking natural neural networks, artificial neural networks (ANN) offer the ability to learn complex non-linear representations with no restrictions on the input features and are ideally suited to a wide variety of exciting use cases, including speech, image...
