SKU: 20176863686

Under Armour Camo Freedom Rival Fleece Amp Hoodie | 1379211

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Description

Under Armour Camo Freedom Rival Fleece Amp Hoodie | 1379211Overview of the Under Armour Camo Freedom Rival Fleece Amp Hoodie fleece hoodie (MPN: 1379211) Cold mornings and long shifts demand a hoodie that stays warm, soft, and ready for duty. The Under Armour Camo Freedom Rival Fleece Amp Hoodie delivers dependable comfort with ultra soft cotton blend fleece and a brushed interior that traps heat without bulk. The loose fit gives you room to move, layer, and work. Ribbed cuffs and bottom hem seal in warmth. A

Overview of the Under Armour Camo Freedom Rival Fleece Amp Hoodie fleece hoodie (MPN: 1379211)


Cold mornings and long shifts demand a hoodie that stays warm, soft, and ready for duty. The Under Armour Camo Freedom Rival Fleece Amp Hoodie delivers dependable comfort with ultra-soft cotton-blend fleece and a brushed interior that traps heat without bulk. The loose fit gives you room to move, layer, and work. Ribbed cuffs and bottom hem seal in warmth. A front kangaroo pocket warms hands and stows small essentials.


Professionals choose this fleece hoodie for station wear, range days, and off-duty life because it balances durability with all-day comfort. The camo pattern adds modern style without shouting. UA Freedom supports and inspires the men and women who protect us every day, so your purchase pays it forward.

Available in Marine OD Green / Khaki Base, Team Royal / Viral Blue, Marine OD Green, and Jet Gray. Sizes run from Small through 3XL. MPN 1379211 ensures you get the exact spec you expect. Great as an everyday sweatshirt or training pullover when the temperature drops.


Key Features

  • Fit: Loose, fuller cut for easy layering.

  • Fabric: 80% cotton, 20% polyester cotton-blend fleece.

  • Warmth: Brushed interior for extra warmth and comfort.

  • Pocket: Front kangaroo pocket for hands and essentials.

  • Finish: Ribbed cuffs and bottom hem for a secure seal.

  • Pattern: Camo design for modern off-duty style.

  • Colors: Marine OD Green / Khaki Base, Team Royal / Viral Blue, Marine OD Green, Jet Gray.

  • Sizes: Small through 3XL.

  • Program: UA Freedom supports first responders and military communities.

  • MPN: 1379211 for accurate product matching.


Compatibility


Pairs with range gear or casual wear. Layers cleanly under a shell or over a shirt. Ideal for training, workouts, travel, and everyday needs.


Frequently Asked Questions about Camo Freedom Rival Fleece Amp Hoodie


Question 1: Is this hoodie warm enough for cool fall and winter days?

Yes. The brushed interior fleece provides reliable warmth without adding bulk.


Question 2: Does it fit true to size?

The loose fit is designed for comfort and layering. Choose your normal size for a relaxed feel.


Question 3: How should I wash it?

Machine wash cold with like colors. Tumble dry low. Do not iron decoration.


Question 4: What colors can I choose from?

Marine OD Green / Khaki Base, Team Royal / Viral Blue, Marine OD Green, and Jet Gray.


Why Buy from WCUniforms


WCUniforms is owned by a former law enforcement officer, EMT, and veterans of the US Marine Corps and Coast Guard. We have used the products we sell in real-world, high-pressure situations, so we know they perform when it matters most. Most orders ship the same day, and we aim to ship in-stock items within 48 business hours. You also get hassle-free 30-day returns (with a few exceptions) and real-time SMS and email tracking updates on every order. Need help? Our team is ready with instant chat during business hours or by phone at 855-452-4440. Shop now and experience the WCUniforms difference with fast shipping, proven gear, and customer support you can trust.


 

UPC MPN SKU COLOR SIZE STOCKED ORIGIN
196883995257 13792113903X 13792113903X Marine OD Green 3X-Large Y MY
196883995271 13792113904X 13792113904X Marine OD Green 4X-Large N MY
196883995400 1379211390LG 1379211390LG Marine OD Green Large Y MY
196883995424 1379211390MD 1379211390MD Marine OD Green Medium Y MY
196883995448 1379211390SM 1379211390SM Marine OD Green Small Y MY
196883995462 1379211390XL 1379211390XL Marine OD Green X-Large Y MY
196883995493 13792113902X 13792113902X Marine OD Green 2X-Large Y MY
196883999415 13792110103X 13792110103X Jet Gray 3X-Large Y MY
196883999439 13792110104X 13792110104X Jet Gray 4X-Large N MY
196883999460 1379211010LG 1379211010LG Jet Gray Large Y MY
196883999484 1379211010MD 1379211010MD Jet Gray Medium Y MY
196883999507 1379211010SM 1379211010SM Jet Gray Small Y MY
196883999521 1379211010XL 1379211010XL Jet Gray X-Large Y MY
196883999552 13792110102X 13792110102X Jet Gray 2X-Large Y MY
196885619908 1379211391SM 1379211391SM Marine OD Green / Khaki Base Small Y MY
196885619922 1379211391MD 1379211391MD Marine OD Green / Khaki Base Medium Y MY
196885619946 1379211391LG 1379211391LG Marine OD Green / Khaki Base Large Y MY
196885619960 1379211391XL 1379211391XL Marine OD Green / Khaki Base X-Large Y MY
196885619984 13792113912X 13792113912X Marine OD Green / Khaki Base 2X-Large Y MY
196885620003 13792113913X 13792113913X Marine OD Green / Khaki Base 3X-Large Y MY
196885626234 1379211400SM 1379211400SM Team Royal / Viral Blue Small Y MY
196885626258 1379211400MD 1379211400MD Team Royal / Viral Blue Medium Y MY
196885626272 1379211400LG 1379211400LG Team Royal / Viral Blue Large Y MY
196885626296 1379211400XL 1379211400XL Team Royal / Viral Blue X-Large Y MY
196885626319 13792114002X 13792114002X Team Royal / Viral Blue 2X-Large Y MY
196885626333 13792114003X 13792114003X Team Royal / Viral Blue 3X-Large Y MY
Shipping Notes
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SKU: 20176863686

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4.6 ★★★★★
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A
Verified Purchase
Amazon Customer
Charlottesville, US
★★★★★ 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
K
Verified Purchase
Kindle Customer
Louisville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Cuba, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Waukegan, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Carnegie, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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