Machine Learning with R
Prepare and process data with H2O and Keras
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How do you teach computers to learn from data? This hands-on introduction teaches the basics of machine learning with R, H2O and Keras using numerous examples. You will be able to select the appropriate approach and apply it to your own questions such as image classification or predictions. Since erroneous data can jeopardize learning success, special attention is paid to data preparation and analysis. For this purpose, R provides highly developed and scientifically sound analysis libraries, the functionality and application of which are shown. You will learn for which applications statistical...
How do you teach computers to learn from data? This hands-on introduction teaches the basics of machine learning with R, H2O and Keras using numerous examples. You will be able to select the appropriate approach and apply it to your own questions such as image classification or predictions. Since erroneous data can jeopardize learning success, special attention is paid to data preparation and analysis. For this purpose, R provides highly developed and scientifically sound analysis libraries, the functionality and application of which are shown. You will learn for which applications statistical methods such as regression, classification, factor, cluster and time series analysis are sufficient and when it is better to use neural networks such as e.g. B. CNNs or RNNs should work. The H20 framework and Keras are used here. Examples show how you can analyze stumbling blocks in the learning process or how to avoid them from the outset. You will also learn under what circumstances you can reuse the results of machine learning and how to do this. This book is a translation of "Maschinelles Lernen mit R", Carl Hanser Verlag, ISBN 978-3446471658
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