PDF-Bücher Deep Learning with R_p1

PDF-Bücher Deep Learning with R_p1

Deep Learning with R_p1

PDF-Bücher Deep Learning with R_p1

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Über den Autor und weitere Mitwirkende

Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.J.J. Allaire is the Founder of RStudio and the creator of the RStudio IDE. J.J. is the author of the R interfaces to TensorFlow and Keras.

Produktinformation

Taschenbuch: 360 Seiten

Verlag: Manning (31. Januar 2018)

Sprache: Englisch

ISBN-10: 9781617295546

ISBN-13: 978-1617295546

ASIN: 161729554X

Größe und/oder Gewicht:

18,4 x 1,9 x 23,5 cm

Durchschnittliche Kundenbewertung:

5.0 von 5 Sternen

3 Kundenrezensionen

Amazon Bestseller-Rang:

Nr. 2.769 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

As a professional statistician in the pharmaceutical industry with a background in predictive modelling and an experience R (but not python up to then) user that needed to get quickly up to speed with the basics of deep learning, I found this book incredibly helpful, very readable, well-written, well explained and very inspiring. The book was not just helpful in terms of the R code provided, but also in terms of the explanations and intuitions the authors shared. It is of course an ideal situation, when one of the key forces behind the popular keras package explains not just how to use his package, but also his knowledge on the topic of deep learning. At the moment, I feel like this is THE book to get, if you intend to use keras in R and still a very good book to pick, if you want to get started with deep learning (if you use phyton, you should probably get the almost identical book by the same author for python).

Das Buch bietet eine sehr gute Einführung in das Thema Neuronale Netze und zeigt im Detail, wie Keras konkret angewendet werden kann. Der Aufbau ist schlüssig. Zunächst werden Maschinenlernen und KI kurzweilig und spannend besprochen. Dann wird die Funktion Neuronaler Netze nicht-technisch dargestellt. Schliesslich folgen praktische Anwendungsbeispiele in R. Ich kann das Buch nur empfehlen! Allerdings konzentriert es sich auf Anwendung. Wer tiefer in die Theorie hinter Neuronale Netze einsteigen will, der wird hier nicht fündig.

Das Buch enthält interessante Beispiele. Die verschiedenen Konzepte (verschiedene Layertypen) werden sehr gut erläutert. Es werden auch weiterführende Ideen angeschnitten.

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