SKU: 19833427711
bosch e bike display keine geschwindigkeitsanzeige

bosch e bike display keine geschwindigkeitsanzeige BOSCH Ebike Sofort - Software

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Description

bosch e bike display keine geschwindigkeitsanzeige BOSCH Ebike Sofort - SoftwareBOSCH Gen1 4 Software Tuning (ohne Chip), sofort via Micro USB Kabel und Browser. Von 25 km h auf 45 Km h freischalten. Kompatibel mit allen Bosch Motoren auer Smartes System Version fr Smart System Fahrrder > hier Dieses Tuning kann sofort nach Kauf per Mikro USB Kabel durchgefhrt werden und hebt die Geschwindigkeitsbeschrnkung des e Bikes auf, sodass dieses die Motoruntersttzung nicht mehr bei 25 km h einstellt. Es muss nichts eingebaut werden. Es

 

 

BOSCH Gen1-4 Software-Tuning (ohne Chip), sofort via Micro USB Kabel und Browser. Von 25 km/h auf 45 Km/h freischalten.

Kompatibel mit allen Bosch Motoren außer Smartes System

Version für Smart System Fahrräder -> hier

Dieses Tuning kann sofort nach Kauf per Mikro-USB Kabel durchgeführt werden und hebt die Geschwindigkeitsbeschränkung des e-Bikes auf, sodass dieses die Motorunterstützung nicht mehr bei 25 km/h einstellt. Es muss nichts eingebaut werden. Es findet keine bauliche Veränderung am Fahrrad statt. Das Tuning ist ein reines Softwaretuning und in einer Minute mit wenigen Klicks erledigt. Der Lizenzschlüssel und der Link zur Tuning-Seite wird sofort per Email zugesendet. Das Tuning-Werkzeug (nur kompatibel mit Chrome und Edge auf Desktop/Laptop, nicht Mobiltelefon) befindet sich unter 1dragon.de

Es muss nichts heruntergeladen werden, da alles im Browser geschieht.

Weitere Details

Nach dem Aktivieren des Tunings wird auf dem Tacho (falls vorhanden) genau die Hälfte der tatsächlich gefahrenen Geschwindigkeit angezeigt. Fährt man 20 km/h, wird auf dem Display des Fahrrads (falls es ein Display hat) 10 km/h angezeigt usw... 

Falls bei der Installation Schwierigkeiten auftreten bitte zunächst die FAQs prüfen: Click

  • Einfache Aktivierung: Zum Aktivieren benötigt man nur einen Computer mit Chrome oder Edge Browser und ein Mikro USB Kabel (das sind die Kabel, die zum Aufladen von Android-Telefonen verwendet wurden, bevor es USB-C gab)
  • Einfache Bedienung: Das Tuning lässt sich jederzeit wieder entfernen. Am Fahrrad wird nichts verändert und das Tuning kann mit wenigen Klicks jederzeit deaktiviert werden. Das Fahrrad ist dann wieder zurück auf Werkseinstellungen.
  • Unsichtbar: Es befindet sich kein Tuning Chip im Fahrrad.
Wichtige Information!

Nicht geeignet für den öffentlichen Verkehrsraum.

Wir übernehmen keinerlei Verantwortung. Nur mit Helm und allen wichtigen Protektoren fahren. Mit Modifikation nur als Sportgerät verwendbar. Keine Haftung für Defekte am Sportgerät und keine Haftung für Folgeschäden von Defekten am Sportgerät.

Benutzung erfolgt auf eigene Gefahr. Fahren mit erhöhter Geschwindigkeit ist gefährlich und erhöht die Verletzungsgefahr.

Zur Sachmängelhaftung bzw. Garantie gelten die gesetzlichen Bedingungen.

WEEE-Reg.-Nr. DE 98802828

Da es sich um einen Lizenzschlüssel für eine Software handelt, ist der Widerruf ausgeschlossen.
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SKU: 19833427711

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4.1 ★★★★★
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William P Ross
Waukegan, 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
New York, 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.
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Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
New York, 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
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mackster
Phoenix, 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.
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Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Whiting, 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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