Person Identification System using Handwritten signatures by ANN - Rane, Jayesh
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We know that computers are better than people at crunching series of numbers, but what about tasks that are more complex? How do you teach a computer what a cat looks like? Or how to drive a car? Or how to play a complex strategy game? Or make predictions about the stock market? These are some of the most difficult tasks in artificial intelligence, far outstripping the capabilities of normal machine learning techniques. In these cases, computer scientists turn to neural networks. What sets neural networks apart from other machine learning algorithms is that they make use of an architecture…mehr

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Produktbeschreibung
We know that computers are better than people at crunching series of numbers, but what about tasks that are more complex? How do you teach a computer what a cat looks like? Or how to drive a car? Or how to play a complex strategy game? Or make predictions about the stock market? These are some of the most difficult tasks in artificial intelligence, far outstripping the capabilities of normal machine learning techniques. In these cases, computer scientists turn to neural networks. What sets neural networks apart from other machine learning algorithms is that they make use of an architecture inspired by the neurons in the human brain. These networks turn out to be well-suited to modeling high-level abstractions across a wide array of disciplines and industries.
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Autorenporträt
Prof. Jayesh Rane arbeitet als Assistenzprofessor in der Abteilung Elektronik und Telekommunikationstechnik. Er promoviert in Elektronik und hat seinen Master of Technology in Kommunikationstechnik abgeschlossen. Künstliche Intelligenz, Bildverarbeitung und Robotik sind seine Forschungsgebiete.