Term of the Moment
digital media server
Definition: deep learning
An AI machine learning architecture employed in "neural networks." Emerging in the 2010s, deep learning is used in all forms of AI such as computer vision, self-driving cars, natural language processing and chatbots. When the resulting system is used to do work, a deep learning model has greater accuracy because the training data were analyzed in greater depth. See convolutional neural network and AI in a nutshell.
Multiple Layers
A simple neural network has one input layer, one "hidden" layer and one output layer. In a deep learning model, the network is trained using many hidden layers, each connected to the other with various constraints.
Phase I - The Training
To train an AI to recognize something, countless examples of similar objects are input to the network, and the connections are refined as each example moves from one layer to the next. By the time the image reaches the final layer, its pattern has been recognized more thoroughly. If the training is "supervised," the training examples are identified (an image of a dog is named "dog"). In an "unsupervised" approach, the deep learning system has to figure out which objects are similar.
Phase II - Inference
The deep learning phase creates the "inference engine," which does the actual processing such as identifying an object. The greater number of layers in the deep learning phase and the larger the number of examples, the more accurate the inference engine and the better the results. See DLA, machine learning, GAN, neural network and TensorFlow.
Algorithms Make Mistakes
Deep Learning in the Hierarchy
Multiple Layers
A simple neural network has one input layer, one "hidden" layer and one output layer. In a deep learning model, the network is trained using many hidden layers, each connected to the other with various constraints.
Phase I - The Training
To train an AI to recognize something, countless examples of similar objects are input to the network, and the connections are refined as each example moves from one layer to the next. By the time the image reaches the final layer, its pattern has been recognized more thoroughly. If the training is "supervised," the training examples are identified (an image of a dog is named "dog"). In an "unsupervised" approach, the deep learning system has to figure out which objects are similar.
Phase II - Inference
The deep learning phase creates the "inference engine," which does the actual processing such as identifying an object. The greater number of layers in the deep learning phase and the larger the number of examples, the more accurate the inference engine and the better the results. See DLA, machine learning, GAN, neural network and TensorFlow.
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).