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Summary
Machine learning algorithms convert mathematical models into code and thus enable the extraction of patterns from data, which can be used for predictions and other purposes. On the other hand, practical framework conditions, such as the physical distribution of data across different storage media, the transport costs of data and computational instructions must be taken into account.
Show Notes
ContentMachine learning algorithms convert mathematical models into code and thus enable the extraction of patterns from data, which can be used for predictions and other purposes.
Computational efficiency is of great importance, since finding optimal model coefficients and hyperparameters often requires a large number of computational operations.
The implementation of machine learning algorithms is in essence the efficient use of numerical operations on suitable data structures.
On the one hand, theoretical considerations regarding the number of operations and runtime can be made.
On the other hand, practical framework conditions, such as the physical distribution of data across different storage media, the transport costs of data and computational instructions must be taken into account.
Source
https://www.fhnw.ch/de/studium/module/9316487%3Fshow_language%3Den
