An artificial neural network (ANN) is a computational model that imitates the biological network of the human body. Artificial neural network (ANN) processing devices can be algorithms or actual hardware that are loosely modeled after the neuronal structure of the mammalian cerebral cortex. It is an interconnected system of artificial neurons. Neural networks have self-learning capabilities and can execute tasks such as clustering, classification, association and prediction.
AI expert Maureen Caudill describes a neural network as "a computing system composed of a number of simple and highly interconnected processing elements that process information by their dynamic state response to external inputs."
Neural networks are applied in various industries, and primarily in the branch of machine learning called deep learning. The following are types of neural networks used in machine learning as well as topics associated with neural networks. Some of neural networks' featured abilities include: identifying patterns or trends in data and prediction, which are suited for sales forecasting; industrial process control; customer research; data validation; risk management; and target marketing in the business sector.
Neural networks are also used in voice recognition, diagnosis of hepatitis, recovery of telecommunications from faulty software, interpretation of words, undersea mine detection, texture analysis, three-dimensional object recognition, handwritten word recognition, image recognition and facial recognition.
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- Natural language processing (NLP)Natural language processing is a branch of artificial intelligence that is concerned with giving computers the ability to comprehend spoken words and text in the same way humans can.
- Reinforcement LearningAn area of machine learning focusing on how machines and software agents react in a specific context to maximize performance and achieve reward known as reinforcement signal.
- Anomaly Detection
- Differentiable neural computerA differentiable neural computer (DNC) is a machine learning model consisting of a a neural network that combines the learning and pattern-recognition strengths of deep neural networks with the ability to retain information in complex data structures.