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Dachträger für Ford Connect II Tourneo L2 (Fixpunkt) (2013--2022) (2x) CRUZ Cargo Xpro 2022 SFKompletter Dachtrger CRUZ Cargo Xpro 2022 SF fr Ford Connect II Tourneo L2 (Fixpunkt) (2013 2022), inkusive Quertrger und Fsse. Schloss mit Schlssel sind optional bestellbar. (Link) Heavy Duty Stahl Dachtrger mit aerodynamischem Profil aus verzinktem Stahl fr leichte Nutzfahrzeuge. Der CRUZ Cargo Xpro Dachtrger hat ein aerodynamisches Profil mit ovalem Querschnitt aus verzinktem Stahl, der mit der sthetik aktueller Fahrzeugmodelle verbindet. Das
Kompletter Dachträger CRUZ Cargo Xpro 2022 SF für Ford Connect II Tourneo L2 (Fixpunkt) (2013--2022), inkusive Querträger und Füsse.
Schloss mit Schlüssel sind optional bestellbar. (Link)
Heavy-Duty Stahl-Dachträger mit aerodynamischem Profil aus verzinktem Stahl für leichte Nutzfahrzeuge.Der CRUZ Cargo Xpro Dachträger hat ein aerodynamisches Profil mit ovalem Querschnitt aus verzinktem Stahl, der mit der Ästhetik aktueller Fahrzeugmodelle verbindet.
Das Design der Profile hat einen besseren Luftwiderstandsbeiwert*, was eine Reduzierung des Geräuschpegels und des Kraftstoffverbrauchs bedeutet.
Die Oberfläche der Profile des CRUZ Cargo Xpro Dachträgers ist mit einer hochwertigen, superbeständigen, texturierten schwarzen Farbe auf Polyesterbasis beschichtet.
Dank des neuen Designs sind die Befestigungsklammern für die Füße nicht notwendig, da die jetzt in seinem unteren Schlitz installiert sind. Dies verbessert die Aerodynamik und eliminiert mögliche Beeinträchtigungen und Reibungen mit der Ladung, so dass der verfügbare Laderaum vergrößert wird.
CRUZ Cargo Xpro Dachträger sind hochbeständig und bieten einen wirksamen und dauerhaften Schutz vor Korrosion und intensiver Nutzung des Produkts bei widrigen Wetterbedingungen.
Einfache Montage von Zubehör, die speziell für den Profi konzipiert sind.
Das hohe Sicherheitsniveau des Systems zur Ladung und für den Transport von Material auf dem Fahrzeugdach, erfüllt die Bedürfnisse professioneller Anwender.
Zur Befestigung auf dem Fahrzeugdach wird das entsprechende CRUZ Kit Befestigungssystem (fahrzeugspezifisch) benötigt.
Suchen Sie in unserem Leitfaden die Marke, das Modell, Karosserie und Baujahr Ihres Fahrzeugs und finden Sie die kompatiblen Lösungen.
(1) im Vergleich zu Profilen mit rechteckigem Querschnitt.
Das Design der Profile hat einen besseren Luftwiderstandsbeiwert*, was eine Reduzierung des Geräuschpegels und des Kraftstoffverbrauchs bedeutet.
Die Oberfläche der Profile des CRUZ Cargo Xpro Dachträgers ist mit einer hochwertigen, superbeständigen, texturierten schwarzen Farbe auf Polyesterbasis beschichtet.
Dank des neuen Designs sind die Befestigungsklammern für die Füße nicht notwendig, da die jetzt in seinem unteren Schlitz installiert sind. Dies verbessert die Aerodynamik und eliminiert mögliche Beeinträchtigungen und Reibungen mit der Ladung, so dass der verfügbare Laderaum vergrößert wird.
CRUZ Cargo Xpro Dachträger sind hochbeständig und bieten einen wirksamen und dauerhaften Schutz vor Korrosion und intensiver Nutzung des Produkts bei widrigen Wetterbedingungen.
Einfache Montage von Zubehör, die speziell für den Profi konzipiert sind.
Das hohe Sicherheitsniveau des Systems zur Ladung und für den Transport von Material auf dem Fahrzeugdach, erfüllt die Bedürfnisse professioneller Anwender.
Zur Befestigung auf dem Fahrzeugdach wird das entsprechende CRUZ Kit Befestigungssystem (fahrzeugspezifisch) benötigt.
Suchen Sie in unserem Leitfaden die Marke, das Modell, Karosserie und Baujahr Ihres Fahrzeugs und finden Sie die kompatiblen Lösungen.
(1) im Vergleich zu Profilen mit rechteckigem Querschnitt.
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4.2 ★★★★★
Based on 24 reviews
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Product Reviews
★★★★★ 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!!
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Reviewed in the United States on November 30, 2025
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on May 22, 2026
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on May 15, 2018