SKU: 46006628472
benotti rennrad test

benotti rennrad test Benotti Fuoco Ultra SE No.1 – Limitiertes Carbon-Rennrad RED AXS – Bici No.1

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Description

benotti rennrad test Benotti Fuoco Ultra SE No.1 – Limitiertes Carbon-Rennrad RED AXS – Bici No.1Benotti Fuoco Ultra Special Edition No. 1 Limitierte High End Performance Das Benotti Fuoco Ultra Special Edition No. 1 ist ein exklusives Carbon Rennrad, das anlsslich des 20 jhrigen Firmenjubilums von Benotti entstanden ist. Der Rahmen dieser limitierten Edition (100 Stck) basiert auf einem besonders aufwndigen Carbonlayup und wird in Deutschland gefertigt fr ein herausragendes Verhltnis aus Steifigkeit, Gewicht und Fahrperformance. Ultra leichter

Benotti Fuoco Ultra Special Edition No.1 – Limitierte High-End Performance

Das Benotti Fuoco Ultra Special Edition No.1 ist ein exklusives Carbon-Rennrad, das anlässlich des 20-jährigen Firmenjubiläums von Benotti entstanden ist. Der Rahmen dieser limitierten Edition (100 Stück) basiert auf einem besonders aufwändigen Carbonlayup und wird in Deutschland gefertigt – für ein herausragendes Verhältnis aus Steifigkeit, Gewicht und Fahrperformance.

Ultra-leichter Carbonrahmen für maximale Agilität

Der nur 730 g leichte Rahmen besteht aus 14 verschiedenen Carbonfasern und bietet extreme Steifigkeit bei gleichzeitig beeindruckendem Komfort. Die spezielle Silber-Lackierung ist auf 100 Stück limitiert und wiegt lediglich 35 g. Das Fahrgefühl ist sportlich, direkt und dennoch angenehm gedämpft – ideal für Rennfahrer, die höchste Performance suchen.

Engage Carbon Laufräder mit 45/55 mm Profil

Statt der Serien-Laufräder kommt dieses Modell mit hochwertigen Engage Carbon Laufrädern (45 mm vorn / 55 mm hinten). Das moderne Aeroprofil sorgt für hohe Steifigkeit, direkte Beschleunigung und hervorragende Rolleffizienz. Die Kombination mit schnellen 28 mm Continental GP 5000 Reifen garantiert enormen Speed bei niedrigem Rollwiderstand.

SRAM RED AXS – Elektronische High-End Gruppe

Die elektronische SRAM RED AXS 2x12 Schaltgruppe liefert präzises, kabelloses Schalten auf Profi-Niveau. Dank Powermeter mit beidseitiger Messung hast du deine Leistung jederzeit im Blick. Die SRAM Paceline X Bremsen mit 160/140 mm Scheiben bieten fein dosierbare und kraftvolle Bremsleistung.

Edles Ax-Lightness Carbon-Cockpit

Das vollintegrierte Ax-Lightness Carbon-Cockpit, bestehend aus Lenker, Vorbau und Sattelstütze, sorgt für höchste Steifigkeit und komfortable Ergonomie. Perfekt abgestimmt auf die sportliche Sitzposition des Fuoco Ultra.

Leicht, steif & kompromisslos schnell

Mit einem Rahmen aus T1000-Carbon, limitierter Sonderlackierung, AXS Schaltgruppe, Carbon-Cockpit und aerodynamischen Engage Laufrädern ist das Fuoco Ultra SE No.1 ein kompromissloses Rennrad für ambitionierte Fahrer. Agilität, Beschleunigung und Komfort sind auf absolutem Top-Niveau.

Highlights

  • Limitierte Sonderedition zum 20-jährigen Jubiläum, 100 Stück gebaut
  • Ultra-leichter Carbonrahmen (ca. 730 g) aus 14 Carbonfaser-Typen
  • Engage Carbon Laufräder 45/55 mm für maximale Aero-Performance
  • Elektronische SRAM RED AXS 2x12 Schaltung mit beidseitigem Powermeter
  • Ax-Lightness Carbon-Cockpit & Ultra leichte Computer Halterung
  • Continental GP 5000 Reifen für extrem niedrigen Rollwiderstand
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SKU: 46006628472

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4.3 ★★★★★
Based on 13 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
Boise, 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
Lowell, 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
Battle Creek, 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
Alexandria, 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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