SKU: 1665287267
sqlab sattel mtb

sqlab sattel mtb SQlab Sattel 611 ERGOWAVE® CrMo 13cm E-MTB Állrounder

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Description

sqlab sattel mtb SQlab Sattel 611 ERGOWAVE® CrMo 13cm E-MTB ÁllrounderSQlab 611 ERGOWAVE CrMo gnstiger MTB Allround Sattel fr jedes Terrain Der SQlab 611 ERGOWAVE CrMo ist der ideale MTB Allrounder fr alle, die lange Touren, technisches Gelnde und sportliche Kontrolle schtzen und dabei ein starkes Preis Leistungs Verhltnis suchen. Entwickelt fr vielseitigen Einsatz bietet dieser Sattel ergonomischen Komfort, gezielte Druckentlastung und zuverlssige Stabilitt auf jedem Terrain. ERGOWAVE Komfort und Kontrolle fr lange

SQlab 611 ERGOWAVE® CrMo – günstiger MTB-Allround-Sattel für jedes Terrain

Der SQlab 611 ERGOWAVE® CrMo ist der ideale MTB-Allrounder für alle, die lange Touren, technisches Gelände und sportliche Kontrolle schätzen – und dabei ein starkes Preis-Leistungs-Verhältnis suchen. Entwickelt für vielseitigen Einsatz bietet dieser Sattel ergonomischen Komfort, gezielte Druckentlastung und zuverlässige Stabilität auf jedem Terrain.

ERGOWAVE® – Komfort und Kontrolle für lange Fahrten

Die bewährte ERGOWAVE®-Form mit ihrer stufenförmigen Sitzfläche verteilt das Körpergewicht gleichmäßig auf die Sitzknochen. Gleichzeitig sorgen die abgesenkte Sattelnase und die zentrale Vertiefung für gezielte Entlastung sensibler Bereiche.

Das Ergebnis ist eine ergonomisch definierte Sitzposition, die Druck reduziert, Kontrolle erhöht und auch auf langen Strecken für spürbaren Komfort sorgt.

Individuelle Passform dank SQlab Sattelbreitensystem

Komfort beginnt mit der richtigen Breite. Der 611 ERGOWAVE® CrMo ist in vier Breiten (12 / 13 / 14 / 15 cm) erhältlich und lässt sich optimal an den individuellen Sitzknochenabstand anpassen.

Nur wenn der Sattel wirklich passt, entsteht eine ergonomische Sitzposition – für bessere Druckverteilung, mehr Kontrolle und höhere Fahrfreude im Gelände.

Perfekter Halt & gezielte Druckentlastung

Die wellenförmige Kontur und das hochgezogene Heck des 611 ERGOWAVE® sorgen für stabilen Halt nach hinten und eine sichere Sitzposition. Der Druck wird gleichmäßig verteilt, während empfindliche Bereiche entlastet werden.

Besonders bei langen Anstiegen und dynamischen Abfahrten verbessert sich die Kraftübertragung spürbar – für effizienten Vortrieb ohne Verrutschen.

Robust, zuverlässig und preisattraktiv

Die CrMo-Streben bieten eine ausgewogene Mischung aus Stabilität und Gewicht und machen den 611 ERGOWAVE® CrMo besonders langlebig. Verstärkte Kanten schützen den Sattel zusätzlich in rauem Gelände.

Die straffe Polsterung liefert direktes Feedback, präzise Kontrolle und gleichzeitig Komfort auf langen Strecken – ideal für ambitionierte Fahrer, die keine Kompromisse eingehen wollen.

Ideal für Trail, Down Country & vielseitigen MTB-Einsatz

Der 611 ERGOWAVE® CrMo richtet sich an Mountainbiker, die einen zuverlässigen, ergonomischen und bezahlbaren Sattel suchen – perfekt für:

  • Trail- und Down-Country-Fahrer
  • All-Mountain- und Marathon-Einsatz
  • Lange Touren mit wechselndem Terrain

Wichtige Merkmale auf einen Blick

  • ERGOWAVE®-Form: Entlastung des Dammbereichs und bessere Kraftübertragung
  • CrMo-Streben: Stark, zuverlässig und preisattraktiv
  • Straffe Polsterung: Direktes Feedback und Langzeitkomfort
  • Verstärkte Kanten: Zusätzlicher Schutz im Gelände

Technische Daten – SQlab 611 ERGOWAVE® CrMo

  • Einsatzbereich: Trail & Down Country
  • Breiten: 13 cm
  • Gewicht: ca. 250 g
  • Länge: ca. 280 mm
  • Bauhöhe: ca. 51 mm
  • Active-Technologie: Nein
  • Material Streben: CrMo Tube (hollow)
  • Sattelschale: Glass Fibre reinforced Polyamide Compound (PA12 + GF)
  • Polster: Light Foam
  • Bezug: F32
  • Härte: 55 SQ-Shore
  • Entlastung im Dammbereich: 65 %
  • Maximale Belastung: 90 kg
  • E-Bike Ready: Nein
  • DIN/ASTM: Kategorie 4
  • Geschlecht: Unisex
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SKU: 1665287267

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4.3 ★★★★★
Based on 28 reviews
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William P Ross
Birmingham, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Houston, 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
Fort Morgan, 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
West Palm Beach, 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
S
Verified Purchase
Stergios Papadimitriou
Natrona Heights, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on August 25, 2018

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