Basics of machine learning
Anna Dawid, Julian Arnold, Borja Requena, Alexander Gresch, Marcin Płodzień, Kaelan Donatella, Kim A. Nicoli, Paolo Stornati 等 29 位
University of Warsaw University of Basel Institute of Photonic Sciences Heinrich Heine University Düsseldorf
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摘要与影响
In this chapter, we describe basic machine learning concepts connected to optimization and generalization. Moreover, we present a probabilistic view on machine learning that enables us to deal with uncertainty in the predictions we make. Finally, we discuss various basic machine learning models such as support vector machines, neural networks, autoencoders, and autoregressive neural networks. Together, these topics form the machine learning preliminaries needed for understanding the contents of the rest of the book.
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工程Advanced Data Processing Techniques
Machine Learning and Data Classification · Neural Networks and Applications