Pre-Publication V2. A machine learning model in which a number of processors are interconnected in a manner suggestive of the connections between neurons in a human brain, "neurons" that process and transmit information able to identify learn patterns in data, using adjustable parameters to produce outputs recognizing weights and biases from data to map inputs to outputs by a process of trial and error, allowing it to potentially generate, enhance, or animate content. Neural Networks are the foundation of deep learning and power many modern AI applications.
Deliberation Summary:
Clarification on the clause describing how the model learns from data as it was difficult to understand.
Pre-Publication V1. A machine learning model in which a number of processors are interconnected in a manner suggestive of the connections between neurons in a human brain, "neurons" that process and transmit information able to learn pattern-recognizing weights and biases from data to map inputs to outputs by a process of trial and error, allowing it to potentially generate, enhance, or animate content. Neural Networks are the foundation of deep learning and power many modern AI applications.