SKU: 3206977657

Fits Chevy C1500/K1500 (88-99) Tow Hook to Shackle Bracket D-Ring Combo RS164

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Description

Fits Chevy C1500/K1500 (88-99) Tow Hook to Shackle Bracket D-Ring Combo RS164Tow Hook to Shackle Bracket D Ring Combo Chevy GMC C1500 K1500 Truck SUV (88 99) Give your vehicle an upgraded recovery option that offers a more aggressive look with Rough Country's Tow Hook to Shackle Conversion Kit! This easy to install upgrade replaces your factory tow hooks with a durable d ring shackle mount that's easier to access and gives the front of your vehicle an eye catching look. Rough Country's Shackle Mount bolts directly to the frame

Tow Hook to Shackle Bracket - D-Ring Combo - Chevy/GMC C1500/K1500 Truck/SUV (88-99)

Give your vehicle an upgraded recovery option that offers a more aggressive look with Rough Country's Tow Hook to Shackle Conversion Kit! This easy-to-install upgrade replaces your factory tow hooks with a durable d-ring shackle mount that's easier to access and gives the front of your vehicle an eye-catching look. Rough Country's Shackle Mount bolts directly to the frame providing the same strength and durability as your factory tow hooks to aid in recovery in any situation. Each mount is powder coated black for a long lifespan of good looks and is designed to work with both factory and many aftermarket bumpers. The mounting point was engineered to accept any 3/4in d-ring shackle, allowing you to customize the look of your vehicle. Featuring a 100% bolt-on installation process, this great-looking upgrade can be installed in a flash! Simply remove the factory tow hooks and install Rough Country's Tow Hook to Shackle Conversion Kit into the factory tow hook mounting points and you're ready to roll! Best of all, each mount is covered by Rough Country's Lifetime Replacement Guarantee!


Features:

  • Converts factory tow hook to d-ring shackle
  • Easy, 100% bolt on installation
  • Bolts directly to frame
  • Fits any factory bumper
  • Fits any standard size 3/4in D-Ring
  • Durable steel construction, powder coated black

Application:

Year Make Model Submodel
1995-1999 Chevrolet Tahoe Base
1992-1999 GMC K1500 Suburban Base
1992-1999 Chevrolet K1500 Suburban Base
1999-1999 GMC Yukon Denali
1999-1999 Chevrolet K1500 LS
1995-1999 Chevrolet Tahoe LS
1995-1999 Chevrolet K1500 Suburban LS
1995-1999 Chevrolet K1500 Suburban LT
1995-1999 Chevrolet Tahoe LT
1994-1999 GMC K1500 Sierra SL
1988-1999 GMC K1500 Sierra SLE
1994-1999 GMC K1500 Sierra SLT
1992-1999 GMC Yukon SLE
1992-1999 GMC K1500 Suburban SLE
1995-1999 GMC K1500 Suburban SLT
1995-1999 GMC Yukon SLT
1988-1998 Chevrolet K1500 Cheyenne
1994-1998 GMC K1500 Sierra Special
1988-1998 Chevrolet K1500 Silverado
1990-1998 Chevrolet K1500 WT
1994-1997 Chevrolet K1500 Base
1996-1997 GMC Yukon SL
1992-1995 GMC Yukon Base
1995-1995 GMC K1500 Sierra SLS
1994-1994 Chevrolet Blazer Base
1992-1994 Chevrolet Blazer Silverado
1992-1994 Chevrolet K1500 Suburban Silverado
1994-1994 Chevrolet Blazer Silverado Sport
1992-1994 GMC Yukon Sport
1992-1993 Chevrolet Blazer Cheyenne
1993-1993 GMC Yukon GT
1988-1993 GMC K1500 Sierra
1988-1993 GMC K1500 Sierra SLX
1993-1993 GMC K1500 Sierra Sport
1988-1992 Chevrolet K1500 Scottsdale
1991-1991 Chevrolet K1500 Sport
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SKU: 3206977657

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4.0 ★★★★★
Based on 15 reviews
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Verified Purchase
Shannon
Natrona Heights, 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
Pawtucket, 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
Port Orchard, 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 14, 2017
M
Verified Purchase
mackster
Alexandria, 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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