Shallow and Deep Learning Principles Scientific, Philosophical, and Logical Perspectives /
Şen, Zekâi.
Shallow and Deep Learning Principles Scientific, Philosophical, and Logical Perspectives / [electronic resource] : by Zekâi Şen. - 1st ed. 2023. - XX, 661 p. 322 illus., 71 illus. in color. online resource.
Acceso multiusuario
Introduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artıfıcıal Intellıgence -- Machıne Learnıng -- Deep Learning -- Conclusion.
This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.
9783031295553
Telecommunication.
Computer science--Mathematics.
Mathematical statistics.
Technological innovations.
Artificial intelligence.
Communications Engineering, Networks.
Probability and Statistics in Computer Science.
Innovation and Technology Management.
Artificial Intelligence.
TK5101-5105.9
621.382
Shallow and Deep Learning Principles Scientific, Philosophical, and Logical Perspectives / [electronic resource] : by Zekâi Şen. - 1st ed. 2023. - XX, 661 p. 322 illus., 71 illus. in color. online resource.
Acceso multiusuario
Introduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artıfıcıal Intellıgence -- Machıne Learnıng -- Deep Learning -- Conclusion.
This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.
9783031295553
Telecommunication.
Computer science--Mathematics.
Mathematical statistics.
Technological innovations.
Artificial intelligence.
Communications Engineering, Networks.
Probability and Statistics in Computer Science.
Innovation and Technology Management.
Artificial Intelligence.
TK5101-5105.9
621.382
