SKU: 99400354999

Polaris Ranger Brake Pads by SuperATV

$53.96 $59.95
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Description

Polaris Ranger Brake Pads by SuperATVGet the Brake Pad You Need Did you know your Polaris Ranger has two pedals? But the one on the left only works if you have good brake pads. Get SuperATVs Polaris Ranger Brake Pads to stop on a dime and stay in control. Whether you need the heavy duty durability of our sintered brake pads or a standard pad for some light cruising, youll find the brake pad replacement you need right here. Sintered Brake Pads When things start getting wild, youll want

Get the Brake Pad You Need
Did you know your Polaris Ranger has two pedals? But the one on the left only works if you have good brake pads. Get SuperATV’s Polaris Ranger Brake Pads to stop on a dime and stay in control. Whether you need the heavy-duty durability of our sintered brake pads or a standard pad for some light cruising, you’ll find the brake pad replacement you need right here.

Sintered Brake Pads
When things start getting wild, you’ll want our sintered brake pads to keep you in control. The sintered copper pads are built to give you superior stopping power in wet or dry conditions and they are incredibly durable.

Standard Brake Pads
Our standard brake pads are built for those that don’t abuse their machine. They shine in dry conditions where they have grip and longevity you can count on. If you’re doing chores and keeping out of nasty trails, our standard brake pads will suit you fine.

WARNING: This product can impact machine operation. Customer and/or user is responsible for ensuring that this product is compatible with their machine as currently configured, properly installed, and understands any impact this product has or might have on the machine's operation.

⚠ California Proposition 65 Warning ⚠
WARNING: This product may contain a chemical known to the State of California to cause cancer or birth defects or other reproductive harm.

Features
  • Uses sintered copper pads for extreme durability
  • Four times more durable than stock
  • Made with a steel backing plate
  • Excellent gripping power in wet or dry conditions

Fitment

Polaris Ranger 400 Front 2010-2014: BP-P-006
Rear 2010-2014: BP-P-006

Polaris Ranger 500 Front 2008-2010: BP-P-010
Rear 2006-2013: BP-P-006

Polaris Ranger 500 Crew Front 2011-2013: BP-P-010
Rear 2011-2013: BP-P-006

Polaris Ranger 500 EFI Front 2011-2013: BP-P-006
Rear 2011-2013: BP-P-006

Polaris Ranger Midsize 500 (2017+) Front 2017+: BP-P-006
Rear 2017+: BP-P-006

Polaris Ranger Midsize 500 Crew Front 2015+: BP-P-006
Rear 2015+: BP-P-006

Polaris Ranger 570 Midsize Front 2014-2021: BP-P-006
Rear 2014-2021: BP-P-006

Polaris Ranger 570 Crew Midsize Front 2014: BP-P-010
Front 2015: BP-P-006
Front 2016-2021: BP-P-010
Rear 2014-2021: BP-P-006

Polaris Ranger 570 Full Size Front 2016+: BP-P-010
Rear 2016+: BP-P-006

Polaris Ranger 570 Crew Full Size Front 2016+: BP-P-010
Rear 2016+: BP-P-010

Polaris Ranger XP 570 Crew (PRO-FIT Cab) Front 2015-2016: BP-P-010
Rear 2015-2016: BP-P-010

Polaris Ranger XP 570 (PRO-FIT Cab) Front 2015-2016: BP-P-010
Rear 2015-2016: BP-P-006

Polaris Ranger 700 Front 2008-2009: BP-P-010
Rear 2008-2009: BP-P-006

Polaris Ranger 700 Crew Front 2008-2009: BP-P-010
Rear 2008-2009: BP-P-010

Polaris Ranger 700 6x6 Front 2008-2009: BP-P-006
Rear 2006-2007: BP-P-006

Polaris Ranger 800 EFI Front 2010-2014: BP-P-010
Rear 2010-2013: BP-P-006

Polaris Ranger 800 6x6 EFI Front 2010-2017: BP-P-010
Rear 2010-2017: BP-P-010

Polaris Ranger 800 Crew EFI Front 2010-2014: BP-P-010
Rear 2010-2014: BP-P-010

Polaris Ranger 800 Midsize Front 2013-2014: BP-P-006
Rear 2013-2014: BP-P-006

Polaris Ranger XP 900 Front 2013-2019: BP-P-010
Rear 2013-2019: BP-P-006

Polaris Ranger XP 900 Crew Front 2014-2019: BP-P-010
Rear 2014-2019: BP-P-010

Polaris Ranger 900 Diesel Front 2011-2014: BP-P-010
Rear 2010-2014: BP-P-006

Polaris Ranger 900 Diesel Crew Front 2011-2014: BP-P-010
Rear 2011-2014: BP-P-010

Polaris Ranger XP 1000 Front 2017+: BP-P-010
Rear 2017: BP-P-006
Rear 2018+: BP-P-007

Polaris Ranger XP 1000 Crew Front 2017+: BP-P-010
Rear 2017-2018: BP-P-010
Rear 2019+: BP-P-007

Polaris Ranger 1000 Front 2020+: BP-P-010
Rear 2020+: BP-P-007

Polaris Ranger 1000 Crew Front 2020+: BP-P-010
Rear 2020+: BP-P-007

Polaris Ranger 1000 Diesel Front 2015-2018: BP-P-010
Rear 2015-2018: BP-P-006

Polaris Ranger 1000 Diesel Crew Front 2016-2018: BP-P-010
Rear 2016-2018: BP-P-010

Polaris Ranger ETX Front 2015+: BP-P-006
Rear 2015+: BP-P-006

Polaris Ranger EV Front 2010+: BP-P-010
Rear 2010-2014: BP-P-006
Rear 2016+: BP-P-010

Polaris Ranger TM Front 2004-2006: BP-P-006
Rear 2004-2006: BP-P-006

Polaris Ranger HST Front 2015-2017: BP-P-010
Rear 2015-2018: BP-P-006

Polaris Military Diesel Ranger Crew Front 2011-2014: BP-P-010
Rear 2011-2014: BP-P-010

Polaris Military Ranger Crew Front 2010-2013: BP-P-010
Rear 2010-2013: BP-P-010

Replaces OEM Part #:
BP-P-010: 1911228, 2203747, 2205949
BP-P-007: 1911197, 2203318
BP-P-006: 2202413, 1910514, 1910672, 2202097

 

CB 7/31/25

Shipping Notes
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Exchange/Return Notes
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SKU: 99400354999

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4.3 ★★★★★
Based on 21 reviews
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Verified Purchase
0x00000000:00000000
West Palm Beach, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
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Verified Purchase
Zygerian99
Fort Morgan, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
S
Verified Purchase
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!!
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Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Alexandria, 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
Lexington, 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

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