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Titlebook: Computational Methods for Deep Learning; Theoretic, Practice Wei Qi Yan Textbook 20211st edition The Editor(s) (if applicable) and The Aut

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https://doi.org/10.1007/978-3-031-42883-8ill introduce why reinforcement learning is thought as a method of deep learning. Then, mathematically, we will introduce optimization and data fitting, and understand how these two subjects could be applied to deep learning, especially reinforcement learning.
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https://doi.org/10.1007/978-3-031-42883-8 a vector to reflect this relationship. Meanwhile, manifold learning, which is emphasized on infinity continuity and was originated from differential geometry, has been applied to nonlinear dimensionality reduction in machine learning.
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https://doi.org/10.1007/978-3-030-61081-4Deep Learning; Machine Learning; Pattern Analysis; Manifold Learning; Machine Vision; Reinforcement Learn
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CNN and RNN,while, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc. In a nutshell, we hope to introduce deep learning from spatial and temporal aspects, deeply explore the knowledge of this state-of-the-art technology.
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Reinforcement Learning,ill introduce why reinforcement learning is thought as a method of deep learning. Then, mathematically, we will introduce optimization and data fitting, and understand how these two subjects could be applied to deep learning, especially reinforcement learning.
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CapsNet and Manifold Learning, a vector to reflect this relationship. Meanwhile, manifold learning, which is emphasized on infinity continuity and was originated from differential geometry, has been applied to nonlinear dimensionality reduction in machine learning.
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Textbook 20211st edition from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evalu
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