SKU: 67044599510

2006 Polaris Sportsman 450 4x4 Browning Drive Belt Transmission Belt 3211077 Generic

$25.89 $28.77
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Description

2006 Polaris Sportsman 450 4x4 Browning Drive Belt Transmission Belt 3211077 GenericPolaris Ranger 400 500 4X4 Sportsman 335 Xplorer 300 Drive Belt Transmission Belt 3211077 Generic Features: 1. Designed for superior fit, performance and durability. Designed and manufactured to exacting specifications which meet or exceed OEM belt performance. 2. These high performance belts provide maximum power transmission and superior clutch variator torque multiplier response. 3. Without clutch recalibration Matches belt shift curves and pulley

Polaris Ranger 400 500 4X4 Sportsman 335 Xplorer 300 Drive Belt Transmission Belt 3211077 Generic

Features:
1.Designed for superior fit, performance and durability.Designed and manufactured to exacting specifications which meet or exceed OEM belt performance.
2.These high-performance belts provide maximum power-transmission and superior clutch/variator torque-multiplier response.
3.Without clutch recalibration - Matches belt shift curves and pulley speed ratios with minimal slip under extreme-torque conditions.
4.Zero clutch/variator spring or fly-weight adjustment required at installation.Tuned your CVT system to get more performance from your machine.
5.Rounded inner cog construction increases belt flexibility and improves clutch pulley intake and out-feed for smoother CVT operation.
6.Model specific trapezoidal top-notch designs improve heat-dissipation for cooler belt and primary/secondary clutch operation.
7.Professional install will be highly recommend,Installation instruction not included.

Specifications:
Condition:Aftermarket 100% Brand New
Material:Aramid fiber
Type:Drive belt
Quantity:1pc
Size(Circumference*Width):1045*30(mm)
Replacement Part Number:3211077
Note: Please check the Manufacturer Part Number before ordering.

Applicable Models:
For Polaris 3211077
For Polaris ATP 330 2004-2005
For Polaris Big Boss 500 6x6 1998-1999
For Polaris Magnum 325 2X4 2000-2002
For Polaris Magnum 325 4X4 2000-2002
For Polaris Magnum 330 2X4 2003-2005
For Polaris Magnum 330 4X4 2003-2006
For Polaris Ranger 400 4X4 2010-2014
For Polaris Ranger 400 Midsize 2013
For Polaris Ranger 400 4x4 Pursuit 2012
For Polaris Ranger 400 Pursuit HO 2011
For Polaris Ranger 500 2x4 2005-2009
For Polaris Ranger 500 4X4 2005-2007
For Polaris Ranger 500 4x4 Limited Edition 2005
For Polaris Ranger 500 4x4 EFI 2006-2009
For Polaris Ranger 500 6x6 2005
For Polaris Scrambler 400 2X4 2000-2002
For Polaris Scrambler 400 4X4 1994-2002
For Polaris Scrambler 500 2X4 1999-2002
For Polaris Scrambler 500 4X4 1999-2012
For Polaris Sportsman 335 4X4 1999-2000
For Polaris Sportsman 335 1998-1999
For Polaris Sportsman 400 2014
For Polaris Sportsman 400 HO 2014
For Polaris Sportsman 400 South Edition HO 2014
For Polaris Sportsman 400 4x4 South Edition HO 2013
For Polaris Sportsman 400 4x4 HO 2007-2013
For Polaris Sportsman 400 4X4 2003-2005
For Polaris Sportsman 450 4X4 2006-2007
For Polaris Sportsman 450 4x4 Browning 2006
For Polaris Sportsman 500 4x4 HO 2001-2013
For Polaris Sportsman 500 Forest 2011-2013
For Polaris Sportsman 500 Forest Tractor 2011-2013
For Polaris Sportsman 500 Pursuit HO 2013
For Polaris Sportsman 500 Touring HO 2011-2013
For Polaris Sportsman 500 LE HO 2012
For Polaris Sportsman 500 4X4 1998-1999/2002
For Polaris Sportsman 500 4x4 RSE 2000/2002
For Polaris Sportsman 500 6x6 2000-2008
For Polaris Sportsman 500 4x4 Remington Special Edition 1999
For Polaris Sportsman 500 4x4 Richard Petty Edition 2002
For Polaris Sportsman 500 4x4 RSE HO 2001
For Polaris Sportsman 500 4x4 DUSE 2001-2002
For Polaris Sportsman 500 4x4 HO EFI 2006-2009
For Polaris Sportsman 500 4x4 Browning HO EFI 2006
For Polaris Sportsman 500 4x4 NBU HO EFI 2007
For Polaris Sportsman 500 4x4 Deluxe HO EFI 2007
For Polaris Sportsman 500 Touring EFI 2008-2013
For Polaris Trail Blazer 250 1999-2003
For Polaris Trail Blazer 330 2008-2013
For Polaris Xplorer 300 4X4 1999
For Polaris Xplorer 400 4X4 2000-2002

Package Included:
1x Drive Belt

Warranty:
Returns: Customers have the right to apply for a return within 60 days after the receipt of the product
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SKU: 67044599510

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4.0 ★★★★★
Based on 28 reviews
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Product Reviews
M
Verified Purchase
Michelle Barcus
Louisville, US
★★★★★ 5
Very well built
Color: White
Yes it was really nice my granddaughter love's the jewelery box
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 18, 2025
B
BlueTeej
Massapequa, US
★★★★★ 4
Lots of storage space, great mirrors, has paint scent
Color: White
You can store a lot in this! There are a lot of good things to say about this. It's cute, has tons of mirror space and room to store jewelry, seems pretty sturdy as far as the wood parts go, and could also be used as Barbie furniture for a girl with a little imagination--a perfect armoire! It's the perfect height for a Barbie doll. So, there is space for non-dangly earrings to be placed in holes, a section under that for necklaces or bigger earrings, four small spots on top for necklaces or other chains, a larger section there, five drawers, four long necklace spots to hang them with catch sections underneath, and a space for a lot of rings. There are also eight hangers for short chains or earrings. Quality wise, it is decent, but maybe not fantastic. Most of the wood aspects seem strong and built well, except the part that stores rings comes out completely and can be a tiny bit of a challenge to put back in a way that it fits right. The cardboard inserts are a little flimsy. I think if a child used this and pulled down on one of the necklace holders, it might not spring back into place, and then would be useless after that. The cardboard insert on the top for the stud earrings also seems like it will get misshapen pretty easily. The biggest negative, and is one I am not sure I can get beyond, but time will tell, is the strong paint odor of it. It smells like a bedroom smells immediately after it gets painted--that kind of smell that tells you perhaps you should sleep somewhere else for a night. That smell fades, though. I hope this smell also fades because it seems too strong to give to a child, and this is advertised as for a woman or girl (which I assume is a child.) So, size wise and usage is generally good for this. If the paint smell fades, I will be fully satisfied with it.
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Reviewed in the United States on October 18, 2025
K
Kirsten
Belleville, US
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it. For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would. The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is. This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 17, 2026
X
Verified Purchase
0x00000000:00000000
Chelsea, 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
Waukegan, 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

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