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/10.1007/978-3-031-42883-8 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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Transfer Learning and Ensemble Learning,In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.

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https://doi.org/10.1007/978-3-031-42883-8while, 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.