SKU: 87162248602

GGB 2018-2023 Polaris RZR XP 1000, RZR RS1 Trail Muffler

$292.50 $325.00
Save 10%
No.2 Bestseller on 2pierres.com >

Pay in installments of $81.25 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Sep 28 - Oct 3

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

GGB 2018-2023 Polaris RZR XP 1000, RZR RS1 Trail MufflerSKU: 64 3256 2 Fits: 2018 2019 RZR XP 1000 MD XP4 1000 MD 2018 2020 RZR RS1 2018 2021 RZR XP 1000 TRACTOR EU ZUG 2018 2021 RZR XP4 1000 2020 RZR XP 1000 TRACTOR EU ZUG 2020 RZR RS1 PS MD XP4 1000 POLAND 2021 2023 RZR 64 XP4 1000 MD RS1 EPS 2021 2022 RZR XP 1000 HIGH LIFTER TRAILS & ROCKS 2021 2022 RZR XP4 1000 HIGH LIFTER 2022 2023 RZR XP 1000 PREMIUM RIDE COMMAND SP EVAP TRACTOR 2022 2023 RZR XP4 1000 PREMIUM S EVAP RIDE COMMAND 2023 RZR XP4 1000

SKU:  64-3256-2

Fits: 

  • 2018-2019 RZR XP 1000 MD/XP4 1000 MD
  • 2018-2020 RZR RS1
  • 2018-2021 RZR XP 1000 /TRACTOR/EU/ZUG
  • 2018-2021 RZR XP4 1000
  • 2020 RZR XP 1000 TRACTOR/EU/ZUG
  • 2020 RZR/RS1 PS MD/XP4 1000 POLAND
  • 2021-2023 RZR 64 XP4 1000 MD/RS1 EPS
  • 2021-2022 RZR XP 1000 HIGH LIFTER/TRAILS & ROCKS
  • 2021-2022 RZR XP4 1000 HIGH LIFTER
  • 2022-2023 RZR XP 1000 PREMIUM/RIDE COMMAND/SP EVAP/TRACTOR
  • 2022-2023 RZR XP4 1000 PREMIUM/S EVAP/RIDE COMMAND
  • 2023 RZR XP4 1000 SPORT

Weight:
Stock Muffler: 21.00 lbs
GGB Trail: 14.45 lbs
Weight Savings: 6.55 lbs

Decibel Levels:
Stock Decibel:
Idle- 83-84.5
3000- 86.8-87.3
5000- 94.1-95.8

GGB Decibel:
Idle- 83.8-86.7
3000-90
5000- 95.3

TRAIL MUFFLER - INCREDIBLE SOUND, STOCK LIKE dB LEVELS

  • Built entirely from 304 Stainless Steel
  • ZERO Packing material
  • ZERO Maintenance
  • Increased HP and Torque
  • Designed to be run with stock programmer
  • Deep tuned exhaust note throughout the entire RPM range
  • Sound will not change over time
  • Direct Bolt-On
  • OEM style spark arrestor
  • Reduced operating temperatures
  • Deep water compatible
  • All factory body panels re-install
  • Easily meets all sound level requirements

The GGB Trail muffler is constructed from 304 stainless steel and is a direct bolt on muffler that gives a deep tuned exhaust note, close to stock dB levels in the mid and high RPM’s and does not require a fuel programmer. The Trail Muffler provides an increase in exhaust flow resulting in 1-3 HP gain with increased torque. Each system is a direct bolt-on fit and allows all factory body panels to be re-installed. Submersion in mud and water will not affect the integrity or the performance of the GGB Trail muffler as its fully welded stainless steel design with ZERO packing is built to withstand the punishment. The GGB Trail muffler is a direct bolt-on, zero maintenance exhaust system that can be run with or without a programmer. The Trail Series will easily meet J1287 sound regulation of 96dBA.

This muffler has been SPECIFICALLY designed for this machine, tested and tuned accordingly to provide optimal exhaust flow and back pressure to allow the engine to efficiently achieve peak power with the stock programmer. There is NO DRONE with our Trail Muffler. The Trail Muffler has an incredible tuned sound to it. Slightly louder than stock at idle for that head turning start up. As the RPMs increase the Trail Muffler reduces sound, keeping measured dB levels close to stock through the middle and upper RPM range while providing a deep tuned exhaust note from idle to wide open throttle. Our design allows for reduced operating temperatures and weight savings compared to stock. 

In hand you will notice the build quality and fit and finish of the product we deliver, and installation is straight forward as each system is a direct bolt-on using factory mounts and all necessary hardware is included.

DM/9-19-23

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 87162248602

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.4 ★★★★★
Based on 9 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Waukegan, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Boise, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Lexington, 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
Birmingham, 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

recommand products

TOCOBO Vita Tone Up Sun Cream SPF50+ PA++++
475.00
EyeNlip AC Clear Cleansing Foam
565.00
Celimax Pore+ Dark Spot Brightening Care Sunscreen
547.50
Balea MEN Deodorant Body Spray Golden Intense
232.50
Lattafa Asad Zanzibar Eau de Parfum
575.00