SKU: 57440759223

Fotobehang - Lets Play - Vliesbehang

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Description

Fotobehang - Lets Play - VliesbehangMet dit unieke Artgeist Lets Play vlies fotobehang heb je een ware aandachtstrekker aan de muur. Het behang staat er echt wat nonchalant bij en zorgt zeker voor extra sfeer in de ruimte. Doordat de print van geode kwaliteit is, komen de details en kleuren dan ook levensecht tot uiting. Voordelen Vlies Fotobehang Artgeist Vlies Fotobehang is duurzaam, water en krasbestendig en makkelijk op de muur te plakken. Vlies fotobehang wordt aangebracht met

Met dit unieke Artgeist Lets Play vlies fotobehang heb je een ware aandachtstrekker aan de muur. Het behang staat er echt wat nonchalant bij en zorgt zeker voor extra sfeer in de ruimte. Doordat de print van geode kwaliteit is, komen de details en kleuren dan ook levensecht tot uiting.

Voordelen Vlies Fotobehang

Artgeist Vlies Fotobehang is duurzaam, water- en krasbestendig en makkelijk op de muur te plakken. Vlies fotobehang wordt aangebracht met lijm.
✓ Vlieseline verbloemt kleine onregelmatigheden van de muur
✓ Het vormt een verwarmende, isolerende laag
✓ Hoge printkwaliteit met een unieke lasertechnologie
✓ UV bestendig
✓ Print is watervast
✓ Duurzaam en veilig (geschikt voor de slaapkamer of kinderkamer).

Eenvoudige montage

Het fotobehang bestaat uit eenvoudig te monteren banen van 50 cm breed. Iedere verpakking met fotobehang bevat een plakhandleiding. Gewicht fotobehang: 120 g/m2. Breedte per baan: 50 cm.
Aantal banen per afmeting (bxh):
- fotobehang 100x70 cm: bestaat uit 2 banen van 50x70 cm
- fotobehang 150x105 cm: bestaat uit 3 banen van 50x105 cm
- fotobehang 200x140 cm: bestaat uit 4 banen van 50x140 cm
- fotobehang 250x175 cm: bestaat uit 5 banen van 50x175 cm
- fotobehang 300x210 cm: bestaat uit 6 banen van 50x210 cm
- fotobehang 350x245 cm: bestaat uit 7 banen van 50x245 cm
- fotobehang 400x280 cm: bestaat uit 8 banen van 50x280 cm

Behangtips Vliesbehang

Met vliesbehang is het behangen heel eenvoudig en een behangtafel is uit den boze. Vliesbehang is opgebouwd uit meerdere lagen en heeft daardoor een aantal grote voordelen:
- het kan met licht vochtige doek worden schoongemaakt
- is veel sterker dan behangpapier
- verwijderen van het vliesbehang gaat ook vrij gemakkelijk
- lijm breng je rechtstreeks aan op de muur.

Hieronder een aantal tips om het behang goed aan te brengen.
- Ondergrond: vetvrij, glad, droog en schoon
- Lijm aanbrengen: deze breng je met een behangroller direct aan op de muur.
- Behangen: breng de strips voorzichtig aan.
- Drogen: langzaam laten drogen
Wil je meer info over het behangen? Lees dan onze uitgebreide behang instructie van het vliesbehang.

Bestelling ontvangen
Controleer voordat je begint met behangen of je alles ontvangen hebt. Leg alle delen uit en leg deze in de juiste volgorde. Controleer of alle delen goed op elkaar aansluiten.
Indien deze niet goed op elkaar aansluiten, meld dit gelijk aan onze klantenservice (eenmaal aan de muur geplakt, kun je fotobehang namelijk niet meer reclameren). Laat het behang 24 uur acclimatiseren voordat je deze aanbrengt om krimpen te voorkomen. Voor een optimaal resultaat dient de temperatuur van de te behangen ruimte tussen de 18-21ºC te zijn.

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SKU: 57440759223

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4.8 ★★★★★
Based on 13 reviews
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Shannon
Louisville, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Draper, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Phoenix, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Louisville, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
M
Verified Purchase
mackster
Natrona Heights, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018

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