SKU: 42569019944

Cometic Chevy Gen1 Small Block V8 .060in HTS Exhaust Mani Gasket Set-1.550in x 1.450in Square Ports

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Description

Cometic Chevy Gen1 Small Block V8 .060in HTS Exhaust Mani Gasket Set-1.550in x 1.450in Square PortsCometic Gasket Inc. Exhaust Gaskets are designed to be used with manifolds or headers. Offered in a range of materials to make sure you are covered with any cylinder head and manifold header combination. All exhaust gaskets are designed to be used without the use of messy sealants. The facing material features 75 percent graphite with Kevlar fiber reinforcements for strength and durability No Sealants Are Required This Part Fits: Year Make Model

Cometic Gasket Inc. Exhaust Gaskets are designed to be used with manifolds or headers. Offered in a range of materials to make sure you are covered with any cylinder head and manifold/header combination. All exhaust gaskets are designed to be used without the use of messy sealants.

  • The facing material features 75 percent graphite with Kevlar fiber reinforcements for strength and durability
  • No Sealants Are Required

This Part Fits:

Year Make Model Submodel
1980-1985,1987-1991 Avanti II Base
1985 Avanti II GT
1980-1981 Buick Century Base
1980-1981 Buick Century Estate
1980-1981 Buick Century Limited
1980-1981 Buick Century Sport
1991-1993 Buick Commercial Chassis Base
1982 Buick LeSabre Custom
1982 Buick LeSabre Limited
1982 Buick LeSabre Limited Edition F/E
1980-1981,1986-1987 Buick Regal Base
1980-1981,1986-1987 Buick Regal Limited
1992-1993 Buick Roadmaster Base
1991-1993 Buick Roadmaster Estate Wagon
1992-1993 Buick Roadmaster Limited
1980-1982 Checker Marathon Base
1980-1982 Checker Marathon Deluxe
1980 Chevrolet C10 Big Ten
1980-1981 Chevrolet C10 Cheyenne
1981-1986 Chevrolet C10 Custom
1980 Chevrolet C10 Custom Deluxe
1981 Chevrolet C10 Deluxe
1980-1986 Chevrolet C10 Scottsdale
1980-1986 Chevrolet C10 Silverado
1981-1986 Chevrolet C10 Suburban Custom
1980 Chevrolet C10 Suburban Custom Deluxe
1981 Chevrolet C10 Suburban Deluxe
1980,1982,1984-1986 Chevrolet C10 Suburban Scottsdale
1980,1982-1986 Chevrolet C10 Suburban Silverado
1980-1981 Chevrolet C20 Cheyenne
1981-1986 Chevrolet C20 Custom
1980 Chevrolet C20 Custom Deluxe
1981 Chevrolet C20 Deluxe
1980-1986 Chevrolet C20 Scottsdale
1980-1986 Chevrolet C20 Silverado
1981-1986 Chevrolet C20 Suburban Custom
1980 Chevrolet C20 Suburban Custom Deluxe
1981 Chevrolet C20 Suburban Deluxe
1980,1982,1984-1986 Chevrolet C20 Suburban Scottsdale
1980,1982-1986 Chevrolet C20 Suburban Silverado
1980-1981 Chevrolet C30 Cheyenne
1981-1986 Chevrolet C30 Custom
1980 Chevrolet C30 Custom Deluxe
1981 Chevrolet C30 Deluxe
1980-1986 Chevrolet C30 Scottsdale
1980-1986 Chevrolet C30 Silverado
1980 Chevrolet Camaro Base
1980-1986 Chevrolet Camaro Berlinetta
1985-1986 Chevrolet Camaro Iroc-Z
1980 Chevrolet Camaro Rally Sport
1981-1986 Chevrolet Camaro Sport
1980-1986 Chevrolet Camaro Z28
1982 Chevrolet Camaro Z28 Indianapolis 500 Pace Car
1986 Chevrolet Caprice Base
1980-1986 Chevrolet Caprice Classic
1986 Chevrolet Caprice Classic Brougham
1981-1982 Chevrolet Caprice Classic Estate
1980-1981 Chevrolet Caprice Classic Landau
1980 Chevrolet Caprice Classic Sport
1980-1982,1984-1986 Chevrolet Corvette Base
1982 Chevrolet Corvette Collector's Edition
1986 Chevrolet Corvette Indianapolis 500 Pace Car
1980-1986 Chevrolet El Camino Base
1981-1986 Chevrolet El Camino Conquista
1980-1981 Chevrolet El Camino Royal Knight
1980-1986 Chevrolet El Camino SS
1983 Chevrolet G10 Base
1980-1986 Chevrolet G10 Beauville
1981-1984,1986 Chevrolet G10 Bonaventure
1980-1986 Chevrolet G10 Chevy Van
1980-1986 Chevrolet G10 Sportvan
1980-1986 Chevrolet G20 Beauville
1983-1984 Chevrolet G20 Bonaventure
1980-1986 Chevrolet G20 Chevy Van
1980-1981 Chevrolet G20 Nomad
1980-1986 Chevrolet G20 Sportvan
1980-1986 Chevrolet G30 Beauville
1983-1984 Chevrolet G30 Bonaventure
1980-1986 Chevrolet G30 Chevy Van
1980-1986 Chevrolet G30 Hi-Cube
1980-1986 Chevrolet G30 Sportvan
1980-1985 Chevrolet Impala Base
1981-1982 Chevrolet Impala Estate
1980 Chevrolet Impala Sport
1980-1981 Chevrolet K10 Cheyenne
1981-1986 Chevrolet K10 Custom
1980 Chevrolet K10 Custom Deluxe
1981 Chevrolet K10 Deluxe
1980-1986 Chevrolet K10 Scottsdale
1980-1986 Chevrolet K10 Silverado
1981-1986 Chevrolet K10 Suburban Custom
1980 Chevrolet K10 Suburban Custom Deluxe
1981 Chevrolet K10 Suburban Deluxe
1980,1982,1984-1986 Chevrolet K10 Suburban Scottsdale
1980,1982-1983,1985-1986 Chevrolet K10 Suburban Silverado
1980-1981 Chevrolet K20 Cheyenne
1981-1986 Chevrolet K20 Custom
1980 Chevrolet K20 Custom Deluxe
1981 Chevrolet K20 Deluxe
1980-1986 Chevrolet K20 Scottsdale
1980-1983,1985-1986 Chevrolet K20 Silverado
1981-1986 Chevrolet K20 Suburban Custom
1980 Chevrolet K20 Suburban Custom Deluxe
1981 Chevrolet K20 Suburban Deluxe
1980,1982,1984-1986 Chevrolet K20 Suburban Scottsdale
1980,1982-1983,1985-1986 Chevrolet K20 Suburban Silverado
1980-1981 Chevrolet K30 Cheyenne
1981-1986 Chevrolet K30 Custom
1980 Chevrolet K30 Custom Deluxe
1981 Chevrolet K30 Deluxe
1980-1986 Chevrolet K30 Scottsdale
1980-1986 Chevrolet K30 Silverado
1980 Chevrolet K5 Blazer Cheyenne
1981-1986 Chevrolet K5 Blazer Custom
1980 Chevrolet K5 Blazer Custom Deluxe
1981 Chevrolet K5 Blazer Deluxe
1984 Chevrolet K5 Blazer Scottsdale
1980-1986 Chevrolet K5 Blazer Silverado
1980-1981 Chevrolet Malibu Base
1980-1983 Chevrolet Malibu Classic
1980 Chevrolet Malibu Classic Estate
1980-1981 Chevrolet Malibu Classic Landau
1980 Chevrolet Malibu Classic Sport
1980 Chevrolet Malibu Estate
1980 Chevrolet Malibu Sport
1981-1986 Chevrolet Monte Carlo Base
1983 Chevrolet Monte Carlo CL
1980-1981 Chevrolet Monte Carlo Landau
1986 Chevrolet Monte Carlo LS
1980 Chevrolet Monte Carlo Sport
1983-1986 Chevrolet Monte Carlo SS
1985 Excalibur Phaeton Anniversary
1983-1984,1986-1989 Excalibur Phaeton Base
1980-1986 GMC C1500 Base
1980 GMC C1500 Heavy Half
1980-1986 GMC C1500 High Sierra
1980-1986 GMC C1500 Sierra Classic
1980-1982 GMC C1500 Sierra Grande
1980-1982 GMC C1500 Street Coupe
1980-1986 GMC C1500 Suburban Base
1980-1986 GMC C1500 Suburban High Sierra
1980-1986 GMC C1500 Suburban Sierra Classic
1980-1982 GMC C1500 Suburban Sierra Grande
1980-1986 GMC C2500 Base
1980-1986 GMC C2500 High Sierra
1980-1986 GMC C2500 Sierra Classic
1980-1982 GMC C2500 Sierra Grande
1980-1986 GMC C2500 Suburban Base
1980-1986 GMC C2500 Suburban High Sierra
1980-1986 GMC C2500 Suburban Sierra Classic
1980-1982 GMC C2500 Suburban Sierra Grande
1980-1986 GMC C3500 Base
1980-1986 GMC C3500 High Sierra
1980-1986 GMC C3500 Sierra Classic
1980-1982 GMC C3500 Sierra Grande
1981-1986 GMC Caballero Amarillo
1980-1986 GMC Caballero Base
1981 GMC Caballero Conquista
1980-1986 GMC Caballero Diablo
1980 GMC Caballero Laredo
1981 GMC Caballero Royal Knight
1980-1986 GMC G1500 Rally
1983-1984 GMC G1500 Rally Custom
1980-1986 GMC G1500 Rally STX
1980-1986 GMC G1500 Vandura
1980-1986 GMC G2500 Rally
1983-1984 GMC G2500 Rally Custom
1980-1986 GMC G2500 Rally STX
1980-1986 GMC G2500 Vandura
1980-1988 GMC G3500 Magnavan
1980-1986 GMC G3500 Rally
1980-1986 GMC G3500 Rally Camper Special
1981-1988 GMC G3500 Rally Custom
1980-1986 GMC G3500 Rally STX
1980-1986 GMC G3500 Vandura
1980-1986 GMC G3500 Vandura Special
1980-1986 GMC Jimmy Base
1980-1985 GMC Jimmy High Sierra
1980-1986 GMC Jimmy Sierra Classic
1980-1982 GMC Jimmy Sierra Grande
1980-1982 GMC Jimmy Street Coupe
1980-1986 GMC K1500 Base
1980-1986 GMC K1500 High Sierra
1980-1986 GMC K1500 Sierra Classic
1980-1982 GMC K1500 Sierra Grande
1980-1986 GMC K1500 Suburban Base
1980-1986 GMC K1500 Suburban High Sierra
1980-1986 GMC K1500 Suburban Sierra Classic
1980-1982 GMC K1500 Suburban Sierra Grande
1980-1986 GMC K2500 Base
1980-1986 GMC K2500 High Sierra
1980-1986 GMC K2500 Sierra Classic
1980-1982 GMC K2500 Sierra Grande
1980-1986 GMC K2500 Suburban Base
1980-1986 GMC K2500 Suburban High Sierra
1980-1986 GMC K2500 Suburban Sierra Classic
1980-1982 GMC K2500 Suburban Sierra Grande
1980-1986 GMC K3500 Base
1980-1986 GMC K3500 High Sierra
1980-1986 GMC K3500 Sierra Classic
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4.2 ★★★★★
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0x00000000:00000000
Battle Creek, 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
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Verified Purchase
Zygerian99
Louisville, 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
Lake Worth, 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
Lexington, 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
Massapequa, 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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