Pay in installments of $13.31 with
,
and
Shipping Estimate
USA
- USA
- CAN
- USA
- CAN
Ships within 48 hours · Estimated delivery Sep 29 - Oct 4
Promo Codes Available:
For Your Every Summer RSVP, with Code: SUMMER15
Description
Mr. Gasket Ultra-Seal Valve Cover Gaskets - 5871Overview: Ultra Seal is Mr. Gasket's premium valve cover gasket material. It's manufactured from a high density cork and rubber blend with a black latex coat applied to the outside to help seal minor flange irregularites and eliminate oil leakage. It creates a positive seal by controlled swelling of the gasket material when exposed to hot oil. For OE replacement, high performance street, drag race and oval track use. Sold as a pair, these gaskets are
Overview:
Ultra-Seal is Mr. Gasket's premium valve cover gasket material. It's manufactured from a high density cork and rubber blend with a black latex coat applied to the outside to help seal minor flange irregularites and eliminate oil leakage. It creates a positive seal by controlled swelling of the gasket material when exposed to hot oil. For OE replacement, high performance street, drag race and oval track use. Sold as a pair, these gaskets are .187" thick.
Features:
Application:
| Year | Make | Model | Submodel | Engine Size |
|---|---|---|---|---|
| 1970 - 1976 | Mercury | Montego | 351/5.8 V8 | |
| 1972 - 1973 | Mercury | Monterey | 351/5.8 V8 | |
| 1971 - 1974 | Mercury | Monterey | 400/6.6 V8 | |
| 1972 - 1976 | Mercury | Montego | 400/6.6 V8 | |
| 1975 - 1976 | Ford | Elite | 400/6.6 V8 | |
| 1975 - 1976 | Ford | Elite | 351/5.8 V8 | |
| 1971 - 1972 | Ford | Custom | 351/5.8 V8 | |
| 1971 - 1972 | Ford | Custom | 400/6.6 V8 | |
| 1971 - 1977 | Ford | Custom 500 | 400/6.6 V8 | |
| 1971 - 1974 | Ford | Country Squire | 351/5.8 V8 | |
| 1971 - 1974 | Ford | Country Squire | 400/6.6 V8 | |
| 1971 - 1977 | Ford | Custom 500 | 351/5.8 V8 | |
| 1970 | Ford | Fairlane | 351/5.8 V8 | |
| 1971 - 1974 | Ford | Galaxie 500 | 400/6.6 V8 | |
| 1970 - 1974 | Ford | Galaxie 500 | 351/5.8 V8 | |
| 1972 - 1976 | Ford | Gran Torino | 351/5.8 V8 | |
| 1972 - 1976 | Ford | Gran Torino | 400/6.6 V8 | |
| 1971 - 1978 | Ford | LTD | 400/6.6 V8 | |
| 1971 - 1978 | Ford | LTD | 351/5.8 V8 | |
| 1970 - 1973 | Ford | Mustang | 351/5.8 V8 | |
| 1970 - 1974 | Ford | Ranch Wagon | 351/5.8 V8 | |
| 1971 - 1974 | Ford | Ranch Wagon | 400/6.6 V8 | |
| 1970 - 1979 | Ford | Ranchero | 351/5.8 V8 | |
| 1972 - 1978 | Ford | Ranchero | 400/6.6 V8 | |
| 1972 - 1978 | Ford | Thunderbird | 400/6.6 V8 | |
| 1970 - 1976 | Ford | Torino | 351/5.8 V8 | |
| 1972 - 1976 | Ford | Torino | 400/6.6 V8 | |
| 1971 - 1974 | Mercury | Colony Park | 400/6.6 V8 | |
| 1972 - 1973 | Mercury | Colony Park | 351/5.8 V8 | |
| 1969 | Mercury | Cougar | 302/5 V8 | |
| 1970 - 1979 | Mercury | Cougar | 351/5.8 V8 | |
| 1973 - 1978 | Mercury | Cougar | 400/6.6 V8 | |
| 1977 - 1978 | Ford | LTD II | 400/6.6 V8 | |
| 1977 - 1979 | Ford | LTD II | 351/5.8 V8 | |
| 1977 - 1979 | Ford | Thunderbird | 351/5.8 V8 | |
| 1977 - 1978 | Lincoln | Continental | 400/6.6 V8 | |
| 1979 | Lincoln | Continental | ||
| 1977 - 1978 | Lincoln | Mark V | 400/6.6 V8 | |
| 1979 | Lincoln | Mark V | ||
| 1978 | Mercury | Grand Marquis | 351/5.8 V8 | |
| 1975 - 1978 | Mercury | Grand Marquis | 400/6.6 V8 | |
| 1971 - 1978 | Mercury | Marquis | 400/6.6 V8 | |
| 1972 - 1978 | Mercury | Marquis | 351/5.8 V8 | |
| 1971 - 1974 | Ford | Country Sedan | 400/6.6 V8 | |
| 1971 - 1974 | Ford | Country Sedan | 351/5.8 V8 | |
| 1970 - 1971 | Mercury | Cyclone | 351/5.8 V8 | |
| 1978 - 1981 | Ford | Bronco | 351/5.8 V8 | |
| 1978 - 1979 | Ford | Bronco | 400/6.6 V8 | |
| 1980 | Ford | E-250 Econoline | 400/6.6 V8 | |
| 1980 - 1981 | Ford | E-250 Econoline | 351/5.8 V8 | |
| 1980 - 1981 | Ford | E-250 Econoline Club Wagon | 351/5.8 V8 | |
| 1980 - 1982 | Ford | E-250 Econoline Club Wagon | 400/6.6 V8 | |
| 1980 - 1981 | Ford | E-350 Econoline | 351/5.8 V8 | |
| 1980 - 1982 | Ford | E-350 Econoline | 400/6.6 V8 | |
| 1980 - 1981 | Ford | E-350 Econoline Club Wagon | 351/5.8 V8 | |
| 1980 - 1982 | Ford | E-350 Econoline Club Wagon | 400/6.6 V8 | |
| 1977 - 1979 | Ford | F-100 | 351/5.8 V8 | |
| 1977 - 1979 | Ford | F-100 | 400/6.6 V8 | |
| 1977 - 1981 | Ford | F-150 | 351/5.8 V8 | |
| 1977 - 1979 | Ford | F-150 | 400/6.6 V8 | |
| 1977 - 1981 | Ford | F-250 | 351/5.8 V8 | |
| 1977 - 1982 | Ford | F-250 | 400/6.6 V8 | |
| 1977 - 1982 | Ford | F-350 | 400/6.6 V8 | |
| 1977 - 1981 | Ford | F-350 | 351/5.8 V8 |
Specs:
| Application | Small Block Ford |
|---|---|
| Brand | Mr. Gasket |
| Emission Code | 5 |
| Engine | Ford Boss 302 |
| Engine | Ford Cleveland |
| Engine | Ford Modified |
| Packaging | Retail - Skin Pack |
| Product Type | Valve Cover Gaskets |
| Valve Cover Gasket Material | Ultra Seal |
| Valve Cover Gasket Thickness | .187" |
| Warranty | Limited 90 Day |
| Weight | 0.5 |
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- 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
4.7 ★★★★★
Based on 19 reviews
Sort
Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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