Gratis Ebook herunterladen Deep Learning (Adaptive Computation and Machine Learning), by Ian Goodfellow Yoshua Bengio

Gratis Ebook herunterladen Deep Learning (Adaptive Computation and Machine Learning), by Ian Goodfellow Yoshua Bengio

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Deep Learning (Adaptive Computation and Machine Learning), by Ian Goodfellow Yoshua Bengio

Gratis Ebook herunterladen Deep Learning (Adaptive Computation and Machine Learning), by Ian Goodfellow Yoshua Bengio

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Pressestimmen

[T]he AI bible... the text should be mandatory reading by all data scientists and machine learning practitioners to get a proper foothold in this rapidly growing area of next-gen technology.--Daniel D. Gutierrez, insideBIGDATA

Über den Autor und weitere Mitwirkende

Ian Goodfellow is Research Scientist at OpenAI. Yoshua Bengio is Professor of Computer Science at the Université de Montréal. Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal.

Produktinformation

Gebundene Ausgabe: 800 Seiten

Verlag: The MIT Press (1. Januar 2017)

Sprache: Englisch

ISBN-10: 0262035618

ISBN-13: 978-0262035613

Größe und/oder Gewicht:

23,1 x 18,3 x 2,8 cm

Durchschnittliche Kundenbewertung:

3.4 von 5 Sternen

24 Kundenrezensionen

Amazon Bestseller-Rang:

Nr. 54 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

I bought this book with quite high hopes on getting a better understanding of deep learning methods. Since many authors have worked on this book many chapters are quite detailled and full of valuable clues on network design and training. In particular, the views on regularization, optimization and the actual 'practitioners guide' chapter are very useful and worth reading (for beginners and seniors alike). However, many of these topics are covered in other books as well and given merely in the context of neural networks. The downside of many chapters is a complete lack of solid mathematical formulation. Sometimes definitions are made, but nothing follows. Hypothesizing, some empirical observations, nothing theoretical.I don't want to blow the 'its not science' horn, here. - Deep Learning has clearly proven to work many times, instead my criticism is that the book falls a bit short to prepare you for many of the complex theories that appear in many scientific publications.In short: this book gives a good overview on machine learning and will certainly help you in applying the techniques in practice. It will not provide you with a conclusive mathematical background.

The book may be the best, most complete and most up to date textbook in the field.However, it is lengthy with lots of theory. Yet lots of chapters are focused on old stuff and specially techniques that authors are known for it. I would prefer a book with better practical coverage and specially industry trends.I am not expecting a code cookbook, as this is a text book, nor a programming guide. However on the other hand, I would prefer to focus on well stablished theories and practices as opposed to a full history of all attempts in the field. There are many places that articles are referred that did this and may be resulted on that, but they have been practically all dead ends which wastes reader times.All in all, this is a great book, but I look forward better ones.

This book thries to give an overview over what has happened in the field of Deep Learning so far. And I think it succeeds. Many readers, also on Amazon, criticize the lack of theory. And they are right. But this is not especially the fault of the authors -- there *is* hardly any theory in the field of Neural Networks. For decades, Neural Network "research" went on like this: turn on the computer, load a model, train the model, test the model, change something, train the changed model, test the changed mode, and so on. The book only reflects this: Why does the nondifferentiable (at 0) ReLU work better than differentiable alternatives? Not the slightest clue. Hey, but it works! Why does Stochastic Gradient seem to be such a big cornerstone of Neural network training? Well...perhaps it enforces flat minima .. but, honestly, not really a clue either. But, hey, it works! It is a triumph of experimentation over reasoning: Every dog has its day, and currently Neural Networks perform better than other methods in many fields of pattern recognition. Let's see what the future brings ...

Nach einer Zusammenfassung der mathematischen Grundlagen (Lineare Algebra, Wahrscheinlichkeitsrechnung und Statistik, Numerische Mathematik) bietet dieses Werk einen breiten Überblick über maschinelles Lernen und neuronale Netzwerke. Dabei führt das Werk an die aktuell verwendeten Verfahren und Modelle heran.Eine exzellente Einführung in dieses Fachgebiet!

Das Buch legt am Anfang die notwendigen mathematischen Grundlagen - Matritzenrechnung und Statistik. Wer einen soliden und tiefen Einstieg in das Thema benötigt oder daran interessiert ist, ist mit diesem Buch gut beraten. Es werden alle wichtige Themen ansprechend und gut erklärt. Ich kann das Buch sehr weiterempfehlen, wenn ein gewisses mathematisches Verständnis vorhanden ist.

Meiner Meinung nach eine der besten Einführungen in das Thema. Die mathematischen Grundlagen sind ebenso beschrieben, wie Optimierungsverfahren oder die wichtigsten Modelle. Es sind die Algorithmen zwar gut beschrieben, aber echte Codebeispiele fehlen. Wer sich damit spielen will, sollte die Theorie mittels PyTorch, Tensorflow oder einem anderen Framework in die Praxis umsetzen.

A copy of the original book with invalid graphs.

Einfach eines der breitesten und tiefsten Buecher in dem Bereich. Kann man nur empfehlen sowohl fuer Anfaenger als auch fuer Profis.

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