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CNN applies the same filters to every "chunk" of the input data. It's applicable when you think every chunk should be processed the same way. For example, we think a face in the top-left of the image should be recognized just as well as a face in the bottom-right. So we do expect to process each part of the image the same way, and a CNN is good for ...


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Any algorithm that uses data (in some form) to improve some performance measure (aka objective function), or to find some function, can be considered a machine learning algorithm. See this answer for more complete definitions of ML. k-means does that. It uses the data to find some division of the data itself into groups, in order to maximize some objective ...


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In general term yes. Because what the ML algorithms do in general is to learn the hidden probability density function of the target examples (cats, dogs..). And that is done by learning the conditional probability function between inputs, $X$, and target outputs, $y$, for discriminative models or by learning the joint probability function for generative ...


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I suppose, the situation is as follows - PReLU increases the expressiveness of a model for a bit at a small cost, but the gain is almost negligible as well (according to this post). There is, indeed, a noticeable difference between ReLU and PReLU, since the former takes the same value for all $\mathbb{R}_{\leq 0}$. However, compared with a LeakyReLU, note ...


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The main similarity between reinforcement learning experience and supervised learning datasets, is that both consist of a set of records. These records are commonly expressed as vectors of numbers for use in the algorithms. In addition, reinforcement learning that uses neural networks (or other function approximation) will typically implement some variant of ...


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I check again with the subchapters of Artificial Intelligence: A Modern Approach, 4th Global ed / US ed from this website the pdf subchapters reference of Global Edition and US Edition. I can confirm you the difference between Global US edition is this subchapter: 20 Knowledge in Learning 739 20.1 A Logical Formulation of Learning 739 20.2 Knowledge ...


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