e mountainbike 90 nm 27,5" Damen E-Bike SUV Trekkingrad Wave LS TERRA CX1 BOSCH PERFORMANCE LINE PX Smart System POWERTUBE 800 Wh 12 x GANG SHIMANO XT RD-M8100 SGS Beige. Größenеmpfehlung 45cm: 165-175cm, 50cm 175-185cm, 55
SKU: 53753916243
e mountainbike 90 nm

e mountainbike 90 nm 27,5" Damen E-Bike SUV Trekkingrad Wave LS TERRA CX1 BOSCH PERFORMANCE LINE PX Smart System POWERTUBE 800 Wh 12 x GANG SHIMANO XT RD-M8100 SGS Beige. Größenеmpfehlung 45cm: 165-175cm, 50cm 175-185cm, 55

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Description

e mountainbike 90 nm 27,5" Damen E-Bike SUV Trekkingrad Wave LS TERRA CX1 BOSCH PERFORMANCE LINE PX Smart System POWERTUBE 800 Wh 12 x GANG SHIMANO XT RD-M8100 SGS Beige. Größenеmpfehlung 45cm: 165-175cm, 50cm 175-185cm, 5527,5" Damen E Bike Suv Trekkingrad Wave LS TERRA CX1 BOSCH PERFORMANCE LINE PX Smart System POWERTUBE 800 Wh 12 x GANG SHIMANO XT RD M8100 SGS Beige Das LS TERRA CX1 27,5" Damen E SUV MTB Wave kombiniert moderne E Bike Technologie mit Komfort und Vielseitigkeit. Der stabile Wave Aluminiumrahmen ermglicht ein besonders bequemes Auf und Absteigen und eignet sich ideal fr Alltag, Pendeln und lngere Touren auf Strae, Radwegen und Schotterstrecken. Bosch

27,5" Damen E-Bike  Suv Trekkingrad Wave LS TERRA CX1 BOSCH PERFORMANCE LINE PX Smart System POWERTUBE 800 Wh 12 x GANG SHIMANO XT RD-M8100 SGS Beige

Das LS TERRA CX1 27,5" Damen E-SUV MTB Wave kombiniert moderne E-Bike-Technologie mit Komfort und Vielseitigkeit. Der stabile Wave-Aluminiumrahmen ermöglicht ein besonders bequemes Auf- und Absteigen und eignet sich ideal für Alltag, Pendeln und längere Touren auf Straße, Radwegen und Schotterstrecken.

Bosch Performance Line PX Smart System

Der leistungsstarke Bosch Performance Line PX Motor (Smart System) liefert bis zu 90 Nm Drehmoment und sorgt für kraftvolle, natürliche Unterstützung und arbeitet besonders leise – ideal für steile Anstiege und längere Strecken.

Der integrierte Bosch PowerTube Akku mit 800 Wh ermöglicht hohe Reichweiten und macht das Bike perfekt für Touren und den täglichen Einsatz.

Über das Bosch Intuvia 100 Display in Kombination mit der LED Remote lassen sich Unterstützungsstufen und wichtige Fahrdaten übersichtlich und komfortabel steuern.

Vielseitiges SUV E-Bike

SUV E-Bikes verbinden die Vorteile von City-, Trekking- und Mountainbikes. Sie bieten Komfort im Alltag und sind gleichzeitig robust genug für Schotterwege, Waldstrecken und längere Touren.

Breitere Reifen, stabile Rahmen und leistungsstarke Motoren machen diese Kategorie besonders vielseitig.

Das LS TERRA CX1 wurde genau für diesen Einsatz entwickelt – komfortabel im Alltag und zuverlässig auf längeren Strecken.

Schaltung, Bremsen & Komfort

Die Shimano Deore XT RD-M8100 12-Gang Schaltung sorgt für präzise Gangwechsel und eine große Übersetzungsbandbreite mit 10–51T Kassette.

Für zuverlässige Bremsleistung sorgen hydraulische Tektro 4-Kolben Scheibenbremsen mit 203 mm Bremsscheibe vorne und 180 mm hinten.

Die RockShox Recon Silver RL Solo Air Luftfedergabel mit 100 mm Federweg sorgt für Komfort und Kontrolle auf unebenen Wegen. Schwalbe SuperMoto Reflex 27,5" Reifen bieten guten Grip und hohe Fahrstabilität.

Vollausstattung

Zur Ausstattung gehören MIK HD Gepäckträger, Curana Aluminium Schutzbleche, Herrmans LED Beleuchtung, ABUS Rahmenschloss, Seitenständer und Klingel.

Mit einem Gewicht von ca. 27,6 kg und einem zulässigen Gesamtgewicht von 140 kg ist das LS TERRA CX1 ein vielseitiges E-SUV Bike für Alltag und Tour.

Rahmenhöhen:
M – 45 cm
L – 50 cm
XL – 55 cm

Verfügbare Herren Fahrrad-Rahmengrößen (Rahmenhöhen): M – 45 cm, L – 50 cm, XL 

55 cm.

Dieses Fahrrad wird zu 98 % fertig montiert geliefert. Es müssen lediglich Lenker und Pedale fixiert werden. Die fahrfertige Endmontage ist von einer fachkundigen Person durchzuführen.

Größenеmpfehlung

Rahmengröße Empfohlene Körpergröße
45 cm 165–175 cm
50 cm 175–185 cm
55 cm 185–195 cm

GEOMETRIE

 

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SKU: 53753916243

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4.5 ★★★★★
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Verified Purchase
William P Ross
Bozeman, 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
Boise, 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
Draper, 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
Chelsea, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 25, 2018

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