An advanced AI machine learning architecture employed by neural networks, often by "convolutional neural networks." Deep learning is used in computer vision, self-driving cars, natural language processing and online advertising. Deep learning enables facial recognition to be more accurate, and it allows medical scans to be interpreted without human analysis. See convolutional neural network
A simple neural network has one input, one "hidden" layer in the middle and one output layer. In a deep learning model, neural networks are trained using multiple hidden layers, each connected to the other with various constraints. For example, in image recognition, countless examples of objects, such as a car, truck, horse or human being, are input to the network, and the connections are refined as the example moves from one layer to the next. By the time the image reaches the final layer, its pattern has been recognized. If the training is "supervised," the image was previously identified as a cat, horse, etc., and the network knows another example of the object.
The deep learning phase creates the "inference engine," which does the actual decision making such as identifying an object. The greater number of layers in the training phase and the larger the number of examples, the more accurate the inference engine and the better the results. See DLA
, machine learning
, neural network
Algorithms Make Mistakes
Deep Learning in the Hierarchy
Deep learning algorithms can be fooled. A famous example is mistaking a muffin for a chihuahua dog. This verification test is used to ensure the viewer is a human, and images such as these can confuse an algorithm. However, more advanced deep learning systems can, in fact, differentiate between them (see CAPTCHA
Deep learning is a part of machine learning, which is a major category of artificial intelligence (AI).