SKU: 13231335970

Pod MX - K4 2.0 Dangerboy Limited Edition 2 Knee Brace (Pair)

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Description

Pod MX - K4 2.0 Dangerboy Limited Edition 2 Knee Brace (Pair)POD DangerBoy LE 2 Knee Brace (Adult) The DangerBoy LE 2 Knee Brace brings PODs proven protection to riders who push harder, jump farther, and expect their gear to hold up to real consequences. Built on PODs K4 platform and finished with exclusive DangerBoy graphics inspired by Haiden Deegan, this brace blends a clean, aggressive style with medically certified knee protection. Its made for riders who want performance gear they can trust when things

POD – DangerBoy LE 2 Knee Brace (Adult)

The DangerBoy LE 2 Knee Brace brings POD’s proven protection to riders who push harder, jump farther, and expect their gear to hold up to real consequences. Built on POD’s K4 platform and finished with exclusive DangerBoy graphics inspired by Haiden Deegan, this brace blends a clean, aggressive style with medically certified knee protection. It’s made for riders who want performance gear they can trust when things get sketchy, not just a brace that looks cool on the shelf.

At the core of the LE 2 is POD’s Human Motion hinge — a system that mirrors how your knee naturally bends and rotates instead of locking it into a rigid position. Inside that hinge are Synthetic Ligaments made from Vectran fibers, engineered to manage the forces that lead to serious knee injuries. The result is a brace that supports your knee during awkward landings, sudden twists, and unexpected impacts, all without feeling bulky or restrictive.

Why We Like It

It gives you real confidence on the bike without feeling overbuilt. The hinge tracks with your knee whether you’re gripping the tank, leaning into a rut, or stretching out over a jump face, and the support only shows up when you need it. The DangerBoy graphics add personality, but the real value is in how natural the brace feels once you’re riding.

Why It’ll Work for You

This brace helps reduce the strain that leads to torn ligaments and joint damage while still letting you move like you normally would. The hinge allows your knee to bend, rotate, and apply pressure without fighting the brace, so you’re not adjusting your riding to make it work. When you land sideways, dab your foot, or catch a rut, the synthetic ligaments take the hit instead of your knee. It supports the joint in the moments that matter and stays comfortable the rest of the time.

Key Features

• DangerBoy limited-edition styling inspired by Haiden Deegan
 • Medically certified knee protection system
 • Human Motion hinge that follows natural knee movement
 • Synthetic Ligaments made from Vectran fibers (5x stronger than steel)
 • Adaptive cuffs designed to flex and fit comfortably
 • Quick Loc & Go straps for easy fastening
 • Lightweight impact-modified frame
 • Suitable for motocross, Supercross, SMX, and multi-sport use

Recommended For

• Injury prevention during impact and awkward landings
• Support for ACL, MCL, PCL, and LCL protection
• Meniscus support and joint stability
• Hyperextension and hyperflexion control

 

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SKU: 13231335970

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4.4 ★★★★★
Based on 20 reviews
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Verified Purchase
Shannon
Pawtucket, 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
Louisville, 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
Fort Morgan, 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
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
Verified Purchase
mackster
Houston, 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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