SKU: 31786471995

Arctic Cat Turn Key Skid

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Description

Arctic Cat Turn Key SkidPower to the ground. Precision when it counts. Comfort on the trail. K Mod gets it done. Overview: Highest performance quick select coupling skid on the market K Mod Specific rails machined by Ice Age. Simply the strongest, lightest rails in the game. K Mod designed control arms Featuring Fox L W QS3 Racing Shocks. Fox shocks come with QS3 compression adjustment as well as rebound, dual rear spring combo with adjustable shift point. K Mod specific

Power to the ground. Precision when it counts. Comfort on the trail. K-Mod gets it done.

Overview:

  • Highest performance quick-select coupling skid on the market
  • K Mod Specific rails machined by Ice Age. Simply the strongest, lightest rails in the game.
  • K Mod designed control arms
  • Featuring Fox L/W QS3 Racing Shocks. Fox shocks come with QS3 compression  adjustment as well as rebound, dual rear spring combo with adjustable shift point.
  • K Mod specific progressively wound springs
  • Four position coupling adjusts in seconds
  • Adjustable limiter strap
  • 141 High Country XF Package Available - Please Call 

As good as the modern mountain sled has become, there is no free lunch when it comes to OEM skid setup. Stock skids are designed to be forgiving, and accommodate a wide range of riders in varying conditions. Unfortunately, this leaves a lot of performance on the table. 

Enter the K-Mod Turn key skid. Bolting this complete skid to your sled will add an incredible amount of precision, control, power to the ground and comfort to your ride.

The secret to K-Mod performance is in our ability to couple the skid depending on what the rider and environment calls for, all backed by the best suspension in the industry. 

Other skids require incredibly harsh setups to control weight transfer. Alternatively some are very comfortable and compliant, but have you trenching all day long. K-Mod allows both a playful, fun, setup when desired, but also a business-like 'get to the top' setup, or a balance between the two all with no adjustment to the shocks. Simply a turn of our coupling system and the behavior of the sled is changed immensely. All done in seconds. This means you get more power to the ground all the while having a comfortable, high performance ride.

Coupling positions are as follows...

  • Stock like handling on #1
  • Need more track in the snow for technical trees and boondocking then use # 2.
  • or the straight up where you need the skis planted and full control, use # 3 or # 4.

While this skid helps a stock sled run better than one could ever imagine, it is a "must" for the high horsepower turbo guys. What good is all that power if you can't effectively get it to the ground?

Built K MOD tough, we have the strongest and most durable suspension on the market. High performance, with multi rate springs, compression adjustable shocks to fit the riding style for every aspect of snowmobiling.

Very adjustable, we can tune in the skid for riders from 130lbs to 300lbs all in the same package with preload and compression adjustments.

Those interested in some amount of skid articulation can add K-Motion, which allows for 4 degrees of flex for easier initiation of a turn while keeping washout to a minimum.

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Exchange/Return Notes
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SKU: 31786471995

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4.5 ★★★★★
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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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Zygerian99
Belleville, 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
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Shannon
Fort Morgan, 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
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Verified Purchase
William P Ross
Fort Morgan, 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
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Verified Purchase
Adam
Los Angeles, 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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