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Description
Sunset Western Jean Cone 14oz Vintage Blue DenimPLEASE NOTE: All markdown items are FINAL SALE. No returns and exchanges can be accepted for these items. It's available to ship immediately. The Sunset Jean is our take on the traditional "rancher" fit Western jean. It features a moderately high rise, fuller thigh, and a subtle bootcut at the bottom. This classic American denim was woven by Cone Mills in North Carolina approximately 12 years ago. Once we saw its perfect blue tone, we purchased
PLEASE NOTE: All markdown items are FINAL SALE. No returns and exchanges can be accepted for these items. It's available to ship immediately.
The Sunset Jean is our take on the traditional "rancher" fit Western jean. It features a moderately high rise, fuller thigh, and a subtle bootcut at the bottom.
This classic American denim was woven by Cone Mills in North Carolina approximately 12 years ago. Once we saw its perfect blue tone, we purchased several deadstock rolls. Its indigo shade is slightly lighter and more vibrant and it has a wonderful hand. The cloth has a strong and pronounced twill structure... meaning that it's going to fade like a beast! This is a bonafide treasure of American textile history.
This style is simultaneously kind of retro and very current. The Sunset Jean offers clean construction, minimal styling without logos, and absolutely gorgeous fabrics for a very competitive price.
It looks boss with a sportcoat and boots. It works great with a white t-shirt and Chuck Taylors. It's a versatile shape and a fresh look for your wardrobe!
This shape is flattering for a range of physiques and we designed it in line with our other shapes. The fit is a bit larger than a Rivet and a bit slimmer than a Wilhelm, meaning the vast majority of our clients can simply order their typical waist size.
If you're new to Epaulet, then please use the chat function of our website for a personal size consultation! We'll be happy to help you.
The Sunset Jean features bartacked seams, aged brass button front & button fly, front pocket rivets, genuine leather rear patch, and our signature rear pocket stitch detail.
Each piece is manufactured from start to finish in Los Angeles. Every jean is cut by Jorge and sewn by Juan and our team of operators.
For this launch project, we're offering 6 gorgeous fabrics.
You can opt for a shorter inseam than our stock 35" length. We recommend ordering 0.5" longer than what you want to allow for shrinkage.
SUNSET JEAN MEASUREMENTS
| Size | Waist | Front Rise | Back Rise | Thigh | Bottom |
|---|---|---|---|---|---|
| 28 | 14.5" | 11.25" | 15.75" | 12" | 9.25" |
| 29 | 15" | 11.25" | 15.75" | 12" | 9.25" |
| 30 | 15.5" | 11.5" | 16" | 12.25" | 9.5" |
| 31 | 16" | 11.5" | 16" | 12.5" | 9.5" |
| 32 | 16.5" | 11.75" | 16.25" | 12.75" | 9.75" |
| 33 | 17" | 11.75" | 16.25" | 13" | 9.75" |
| 34 | 17.5" | 12" | 16.5" | 13.25" | 10" |
| 35 | 18" | 12" | 16.5" | 13.5" | 10" |
| 36 | 18.5" | 12.25" | 16.75" | 13.75" | 10.25" |
| 37 | 19" | 12.25" | 16.75" | 14" | 10.25" |
| 38 | 19.5" | 12.5" | 17" | 14.25" | 10.5" |
| 39 | 20" | 12.5" | 17" | 14.5" | 10.5" |
| 40 | 20.5" | 12.75" | 17.25" | 14.75" | 10.75" |
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- Free Standard Shipping on $100+ Orders to the USA.
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
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4.4 ★★★★★
Based on 10 reviews
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Product Reviews
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid.
I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets.
As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before.
I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt.
PROS:
- Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way.
- Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops.
- Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages.
- Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them.
- PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly.
CONS:
- Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste.
- Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
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Reviewed in the United States on February 26, 2022