SKU: 7336318960
carbon mountainbike frame

carbon mountainbike frame 29er Carbon MTB Bike Hardtail Frame M17 L(19 inch) / UDM

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Description

carbon mountainbike frame 29er Carbon MTB Bike Hardtail Frame M17 L(19 inch) / UDMThe features of 29er Hardtail MTB Frame M17 Tapered headtube reduce cross section area, improve performance, reduce wind resistance and enhance aerodynamic effect BB92 buttom bracket, wider and stronger torsional rigidity Direct mount front derailleur get the best quality and reliability 12x142mm Rear Spacing 2 year warranty for manufacturer defects, lifetime crash replacement program ( discount for new frame ) Fancy cross country mountain biking?

The features of 29er Hardtail MTB Frame M17
  • Tapered headtube-reduce cross section area, improve performance, reduce wind resistance and enhance aerodynamic effect
  • BB92 buttom bracket, wider and stronger torsional rigidity
  • Direct mount front derailleur-get the best quality and reliability
  • 12x142mm Rear Spacing
  • 2 year warranty for manufacturer defects, lifetime crash replacement program ( discount for new frame )

Fancy cross-country mountain biking? Want a waif-like carbon steed to help you to a step on the  podium? But you do not want to spend a crazy amount to make this happen? We have built you your dream frame then. Our M17 carbon fibre XC mountain bike frame is just for you.

We have used aerospace grade carbon fibre to make sure that the ride quality of the M17 frame is out of the world. We also know that even if a frame is comfortable that alone will not win races, so we have worked hard to make sure the frame is light and super stiff where you need it to be stiff. We even made sure that your cables stay hidden through our internal routing.

To create a more substantial bottom bracket area we have used a BB92 bottom bracket shell. This gives our engineers more space to play with and look at the size of our chainstays, and down tube, your pedaling effort will not be flexing ant of these carbon tubes. Our chain stays run all the way to a 142 x 12mm rear thru axle, meaning your wheel will have no room to move it will be held tightly in place.

The downtube mates up with our tapered headtube, you will have no problem finding a fork to fit this XC beast. This frame will then go precisely where you point it, and it will not let you take B-lines you will be A-line through all the courses. Our carbon seat stays, and top tube though are designed to help manage the stiffness from below and cancel out any vibrations, adding a bit of vertical compliance to your ride.

Specification

Type Mountain Bike Frame
Wheel Size 29ER
Material T700 carbon fiber
Weave UD
Finish Clear Coating
 Frame Weight 1173g(17 inch)
Frame Size 15/17/19/21''
Headset Top 1-1/8",down 1- 1/2"
BB BB92
Model M17
Seatpost 31.6mm
Seatpost Clamp 37 mm
Max Tires 29 x 2.3''

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SKU: 7336318960

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4.2 ★★★★★
Based on 19 reviews
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Verified Purchase
Shannon
Birmingham, 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
Pawtucket, 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
Boise, 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
Waukegan, 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
Port Orchard, 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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