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Applied Math for Machine Learning Basics by sf.arthur is a document available to read on EtoBox.

This document introduces the basic mathematical concepts needed for deep learning, including defining functions of many variables, finding maxima and minima, and quantifying probabilities. It describes the goals of machine learning as specifying a model representing beliefs, designing a cost function measuring how well the model matches reality, and using training to minimize the cost. This framework forms the basis for many machine learning algorithms, including deep learning approaches developed later in

Author

sf.arthur

Language

EN