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The Hundred-Page Machine Learning Book, by Andriy Burkov
Bücher Kostenlos The Hundred-Page Machine Learning Book, by Andriy Burkov
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Produktinformation
Taschenbuch: 159 Seiten
Verlag: Andriy Burkov (13. Januar 2019)
Sprache: Englisch
ISBN-10: 199957950X
ISBN-13: 978-1999579500
Größe und/oder Gewicht:
19 x 1 x 23,5 cm
Durchschnittliche Kundenbewertung:
4.7 von 5 Sternen
13 Kundenrezensionen
Amazon Bestseller-Rang:
Nr. 1.795 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
I enjoyed reading the book; even if your Math class in college has been a while you will understand what's going on without missing important ideas. But it's quite superficial on most topics, has almost zero code examples and just touches on the topics very lightly. It also missed out quite a few popular concepts you may want to wrap your head around a bit more. Considering what else is out there that's really weak. It's nice the authors provide QR codes - but I am simply not a QR code gut reading a printed book, scanning something with my phone and then reading a paper on my phone...The book is easily accessible for some important concepts - that's a big bonus. I would say it probably covers 50+% of an introductory class to ML almost to a level expected at university (it lacks depth). But to be honest, the lack of code to better understand the concepts is entirely unacceptable these days... It gives you this warm "ah, I understand that idea now" feeling - but you will be rather puzzled when you have to write a line of code after reading the book and "understanding" the concepts... To the author's defence: you can download the eBook for free from his homepage. I would not buy it again.If you are interested in more material, sample notebooks and want to save some money, you may go with the "Dive into Deep Learning" by Smola et al. Or "Hands-On Machine Learning..." by Géron.
Bei Weitem das Beste Buch wenn es darum geht in das Thema "Machine Learning" einzusteigen, und dabei die nötige Tiefe zu erhalten um selbständig weiter zu recherchieren. Natürlich kann auf ein wenig mehr als 100 Seiten nicht die mathematische Tiefe wie in dem Buch von Goodfellow et al. erreicht werden. Auch in Sachen praktische Anwendung bestechen Bücher wie diese von Géron oder gar Chollet wenn es um spezifische Anwendung von verschiedenen Frameworks geht. Wenn es aber darum geht in möglichst kurzer Zeit (<10 Stunden) mit einem professionellen Wissen in einem Auditorium von Datenanalytikern und Anwendern von Machine Learning oder Deep Learning Algorithmen mitzuwirken, ist das Buch einfach unschlagbar.
This book first and foremost is for readers that work in the realm of data science. You need to know your math to be able to follow the content right away. Prepare to refresh your skills if you want to fully take advantage of this well-written book.Nevertheless, it also gives consultants as well as people working at the intersection of business and data, valuable insights into the world of machine learning. As there are so many busszwords floating around, Andriy Burkov clears up the blurr and provides clear and crisp definitions that will help you to have better conversations about ML in general, as well as understanding the capabilities of current and potentual future solutions.
Whether you are a total beginner or experienced in machine learning, this book must be in your collection.The author gave a sufficient explanation of various topics in machine learning, such that a beginner can truly know what's there in this field, and an experienced machine learning person can rapidly go through and keep his knowledge refreshed.This book came at the right time with the right content, knowing that many machine learning books with "boilerplate" content are out there. Congrats to the author for the easy-to-follow, straight to the point presentation of material.
This book is just a start, but a perfect one. The art of focusing on the most important parts makes it so valuable and digestible for the reader. The reduction to the bare minimum to get the point helps non-data scientists get a foot in the door of this extremely exciting space. Well done! (The price per page seems high, but the condensed content is actually what made it hard to write, I would argue)
I recommend this book to everyone from beginners to professionals!I see its biggest value in the fact that it contains a ton of methods that actually work (not only from his but also my experience) and that you can read through it in one go. Of course most of the knowledge is out there on the web and in other books, but who has the time to distill the signal from all that noise? Also, most machine learning books are rather made to be read on a topic by topic basis from my experience. I.e., you work on say HMM's and so you dig out the book chapters containing that info and go with it. That's all fine, but I get a lot of satisfaction and inspiration out of technical books which can be digested in little time and provide you with knowledge you might not necessarily need today or challenges the way you've tackled things in the past.I don't know Andriy personally, but reading the book felt like having a good long conversation about the methods he knows and relies on.
Great resource for the introduction to the world of machine learning. It gives you basic understanding to the math and techniques for machine learning in condensed format. I can recommend this to anybody who wants get good overall introduction to the topic.
The modern AI space is developing very fast. It's rather hard to be aware of all recent developments. The book contains main machine leaning methods that will work for 95% of applications. The language is not for everyone: some mathematical notation, perhaps lacks business context. This is fine for me. It's not intended for a general reader or non-technical people. Contains, many links to useful resources.
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