A Theory Explains Deep Learning
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| Abstract |
This is our journal for developing Deduction Theory and studying Deep Learning and Artificial intelligence. Deduction Theory is a Theory of Deducing World’s Relativity by Information Coupling and Asymmetry. We focus on information processing, see intelligence as an information structure that relatively close object-oriented, probability-oriented, unsupervised learning, relativity information processing and massive automated information processing. We see deep learning and machine learning as an attempt to make all types of information processing relatively close to probability information processing. We will discuss about how to understand Deep Learning and Artificial intelligence and why Deep Learning is shown better performance than the other methods by metaphysical logic.
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| Keywords | Information theory Computational logic Metaphysics |
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References found in this work BETA
Introduction to Mathematical Philosophy.Bertrand Russell - 1920 - Revue Philosophique de la France Et de l'Etranger 89:465-466.
Begriffschrift, eine der Arithmetischen nachgebildete Formelsprache des reinen Denkens.Gottlob Frege - 1879 - Revue Philosophique de la France Et de l'Etranger 8:108-109.
High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology.David J. Chalmers, Robert M. French & Douglas R. Hofstadter - 1992 - Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.
Introduction to mathematical philosophy.Bertrand Russell - 1920 - Revue de Métaphysique et de Morale 27 (2):4-5.
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