SKU: 95317114750

18mm Orange "High Visibility" Search and Rescue (SAR) NATO Military Watch Strap

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Description

18mm Orange "High Visibility" Search and Rescue (SAR) NATO Military Watch StrapSKU Code: NATO18MMORGE This classic NATO watchband is made from fast drying ballistic nylon webbing, a high tech material that is robust, comfortable, and long lasting, making it ideally suited for military watch straps. The design characteristics of these straps reduce the risk of losing a watch to virtually zero, as the watch is held securely at two different points, making it virtually impossible for it to come off the wrist, even during extreme

SKU Code: NATO18MMORGE

This classic NATO watchband is made from fast-drying ballistic nylon webbing, a high-tech material that is robust, comfortable, and long-lasting, making it ideally suited for military watch straps. The design characteristics of these straps reduce the risk of losing a watch to virtually zero, as the watch is held securely at two different points, making it virtually impossible for it to come off the wrist, even during extreme activities.

History and Evolution

The history of the NATO strap began over 50 years ago with a single grey strap. It initially expanded to include colors such as RAF Blue, Black, Navy Blue, the James Bond pattern, and Olive Green. Since then, we have developed an extensive range of variants in regimental colors, Search and Rescue (SAR) orange, national colors, police unit colors, and various camouflage options for specific situations. These straps are frequently used by military units outside the UK and are popular among serving military personnel for a wide variety of military watches.

Extensive Range

Currently, we manufacture nearly 200 different variants in a wide array of colors and sizes from 18mm to 24mm. These straps are available for retail and wholesale customers, and we also offer custom regimental straps. Many of these straps are sold in large quantities through various procurement contracts.

Specifications and Standards

The original specification for the NATO strap was established by the UK Ministry of Defence as Defence Standard 66-47 Issue 2, published on March 30, 2001. Previous specifications included Def Stan 66-47 Issue 1 (November 13, 1992), Def Stan 66-15 (Part 1) Issue 1 (November 30, 1973), and Def Stan 66-15 (Part 2) Issue 1 (January 31, 1974). The current specification data sheet can be downloaded here. Although minor adjustments have been made, the design remains largely consistent, however some bulk procurement contracts specify stitching around the buckles, others opt for the traditional heat sealed process but both have their advantages and drawbacks and in terms of day-to-day use and general functionality both processes work equally well.

Versatility and Durability

For over forty years, we have supplied these straps to a diverse range of clients, including military personnel, police, emergency services, exploration units, Search and Rescue teams, oil and gas companies, marine salvage and exploration businesses, and organizations operating in tropical or desert locations. The NATO strap's easy maintenance, quick drying time, and resistance to rot and deterioration make it superior to leather or silicone alternatives.

Compatibility

NATO straps are compatible with watches from all leading military contractors, both past and present, such as Marathon, CWC, Citizen, and Pulsar. They are also ideal for robust civilian watches from brands like Seiko, Rolex, and Breitling.

In summary, the NATO strap's unmatched durability, versatility, and secure design make it the preferred choice for a wide range of professional and personal applications.

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SKU: 95317114750

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Shannon
Waukegan, 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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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.
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Reviewed in the United States on March 15, 2017
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Adam
New York, 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.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Bozeman, 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!!
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Reviewed in the United States on July 14, 2017
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mackster
Carnegie, 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.
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Reviewed in the United States on May 15, 2018

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