Titlebook: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R; Frank Emmert-Streib,Salissou Moutari,Matthias Dehm Textbo

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General Prediction Modelsis then used in the presentation of the following chapters. Finally, we discuss a fundamental statistical characteristic that holds for every prediction model. We will see that every output of a prediction model is a random variable.

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Datale if one has a sufficient understanding of the underlying phenomena, the data generation process, and the related experimental measurements. For this reason, we describe in this chapter five different data types and the fields from which they come.

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Deep Learningpter, we discuss major architectures of deep neural networks, including deep feedforward neural networks, convolutional neural networks, deep belief networks, autoencoders, and long short-term memory networks.

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Statistical Inferencestics is to quantify the amount of uncertainty around the conclusions that are made based on a sample of data. In general, . is the (systematic) process of making predictions about a population, using data drawn from that population.

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,Unión Patriótica: the Official Party,is then used in the presentation of the following chapters. Finally, we discuss a fundamental statistical characteristic that holds for every prediction model. We will see that every output of a prediction model is a random variable.