SKU: 66517349209

cooler master qube 500 flatpack 1

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Description

cooler master qube 500 flatpack 1MAAK HET OP JOUW MANIER QUBE 500 Flatpack Zwart Wit Editie Maak kennis met de gloednieuwe QUBE 500, een zeer aanpasbare flatpack hoes van Cooler Master waarmee u uw persoonlijke stijl kunt benadrukken Volledig modulair Modulaire panelen die het gebruikelijke voorbereidingsproces van de koffer vereenvoudigen en stroomlijnen. Elk paneel is afzonderlijk verwijderbaar voor eenvoudig onderhoud. Flatpack montage Naast de duidelijke ecologische voordelen,

MAAK HET OP JOUW MANIER
QUBE 500 Flatpack Zwart-Wit Editie
Maak kennis met de gloednieuwe QUBE 500, een zeer aanpasbare flatpack-hoes van Cooler Master waarmee u uw persoonlijke stijl kunt benadrukken

Volledig modulair
Modulaire panelen die het gebruikelijke voorbereidingsproces van de koffer vereenvoudigen en stroomlijnen. Elk paneel is afzonderlijk verwijderbaar voor eenvoudig onderhoud.

Flatpack-montage
Naast de duidelijke ecologische voordelen, zijn wij ervan overtuigd dat plat inpakken een leuke en bevredigende bouwervaring oplevert.

Welkom in de toekomst van PC DIY.
Verwisselbare zijpanelen
Dankzij het symmetrische ontwerp kunt u het stalen paneel en het glazen paneel met elkaar verwisselen voor een ondoorzichtige constructie.<;p>

Zeer aanpasbaar
Een ongekend niveau van aanpasbaarheid zorgt voor volledige creatieve expressie.

Bouw terwijl je uitpakt
Vanaf het moment dat u de doos opent, begint het bouwen met leuke en eenvoudige stapsgewijze instructies.

Specificaties van normaal formaat en een kleine behuizing
De Qube 500 kan vrijwel alles huisvesten wat je van een grotere behuizing mag verwachten, van EATX-moederborden en de nieuwste GPU's tot twee 280mm-radiatoren.

Hardwarecompatibiliteit
De Qube 500 is compatibel met 280mm waterkoelers, 172mm luchtkoelers, 173mm ATX PSU's, E-ATX moederborden en kleinere modellen, en 360mm GPU's.

Opslagcompatibiliteit
De Qube 500 biedt maximaal 4x 3,5" HDD-montageposities en maximaal 3x 2,5" SSD-montageposities.

Kleur op jouw manier
Geef je volledig over aan je persoonlijkheid met de multi-color Macaron-editie. Deze bevat twee sets extra panelen, een extra stuur en twee accessoirehaken. Mix en match de modulaire panelen om een ​​koffer te creëren die helemaal van jou is.

Extra luchtstroom
De radiatorbevestiging aan de zijkant heeft twee standen, waardoor u een 280mm-radiator kunt installeren of uw GPU van extra luchtstroom kunt voorzien.

Ingebouwde verticale GPU-beugel
Monteer je GPU verticaal met de meegeleverde beugel. Je kunt zelfs de afstand tot het zijpaneel aanpassen voor een goede luchtstroom.

Accessoire ecosysteem
De Qube 500 wordt geleverd met een dynamisch accessoire-ecosysteem. Een Gem mini-kabelmanager en accessoirehaak zijn inbegrepen, en we hebben een aantal 3D-printbare bestanden voor je klaargezet om je eigen accessoires te maken.

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SKU: 66517349209

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4.8 ★★★★★
Based on 29 reviews
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Shannon
Alexandria, 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
Phoenix, 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
Draper, 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
West Palm Beach, 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
Lowell, 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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