Titlebook: Applications of Game Theory in Deep Learning; Tanmoy Hazra,Kushal Anjaria,Akshara Kumari Book 2024 The Editor(s) (if applicable) and The A

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书目名称Applications of Game Theory in Deep Learning影响因子(影响力)




书目名称Applications of Game Theory in Deep Learning影响因子(影响力)学科排名




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书目名称Applications of Game Theory in Deep Learning网络公开度学科排名




书目名称Applications of Game Theory in Deep Learning被引频次




书目名称Applications of Game Theory in Deep Learning被引频次学科排名




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书目名称Applications of Game Theory in Deep Learning年度引用学科排名




书目名称Applications of Game Theory in Deep Learning读者反馈




书目名称Applications of Game Theory in Deep Learning读者反馈学科排名

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Introduction,modeled and analyzed. This sets the stage for understanding various fields’ complex scenarios and decision-making processes. Subsequently, we transition into the realm of deep learning. Here, we dissect the fundamental concepts and algorithms that constitute the backbone of deep learning, providing

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Book 2024our digital age, where algorithms and autonomous agents are becoming more common, the combination of game theory and deep learning has opened a new frontier of exploration. The combination of these two disciplines opens new and exciting avenues. We observe how artificial agents can think strategical

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M. Michael Umble,Elisabeth J. Umblell machines about the different features that help machines to classify between different species. For example, to classify samples from the mixture of guava and apple, features such as color, size, shape, etc. play an important part. However, in the case of DL, features are picked by a neural network without interference from humans.

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Noncooperative Game Theory,ependence.” The term noncooperative game theory was first used in 1951 by John Nash in an article in the journal . Noncooperative game theory includes the number of players, objective function, actions and constraints imposed on the players, and outcome of a probabilistic event.

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Applications of Game Theory in Deep Neural Networks,ll machines about the different features that help machines to classify between different species. For example, to classify samples from the mixture of guava and apple, features such as color, size, shape, etc. play an important part. However, in the case of DL, features are picked by a neural network without interference from humans.

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