SKU: 61079465213
junior high dance dresses

junior high dance dresses Orange inexpensive waltz dance competition dresses sexy Smooth competi – Smarts Dance

$18.48 $20.53
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Size: 4
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Description

junior high dance dresses Orange inexpensive waltz dance competition dresses sexy Smooth competi – Smarts DanceCategory Ballroom Dresses Style Triangle Colors Orange Size Custom Size Fabric Satin Neckline U neck Sleeve Long sleeve Back style V back Floater Wing in back Accessories Neck ring Embellishment Sequin Underpants With underpants Built in Bra With built in bra Fabric: Often we use Chiffon and Lycra, Satin, and Mesh. For more detail Please check our Fabric swatch. Bra cups & underpants underneath with snap on buttons. Color: You can change the color as

 

Category Ballroom Dresses
Style Triangle
Colors Orange
Size Custom Size
Fabric Satin
Neckline U neck
Sleeve Long sleeve
Back style V back
Floater Wing in back
Accessories Neck ring
Embellishment Sequin
Underpants With underpants
Built-in Bra With built-in bra


Fabric: Often we use Chiffon and Lycra, Satin, and Mesh.
For more detail Please check our Fabric swatch.

Bra cups & underpants underneath with snap-on buttons.

Color: You can change the color as you like. The color and fabric can be chosen.
Please choose a color from an online color chart.
And let us know the color No. when you purchase.

Stone: The price is for Korean stone. we can add stone one the dress. please select the stone option that you like.  stone swatch.

Size: Customize/Regular for option
When you purchase, please send your measurement together with your order No. and name to our sale department.

All of our dresses are hand-made, NOT machine no identical products can be made.
Please kindly note that all decorations including flower appliques, stone patterns, neck, arm accessories (if any) will vary during production.
Our designers will determine how to put them on so as to get the best effect.

 Leadtime: We ship worldwide via DHL/TNT/UPS.

Production time is about 15-20 days after confirming customer's payment.

Customize style: If you need to Customize style or some modification for style. Please send the modification via email

Note: Require any, Please write an order comment or send us via email.

If you have any questions or F.A.Q., you can go to our Support Center.
Please allow 12-24 hours for us to respond to your inquiries, as there is a time difference.

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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]
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SKU: 61079465213

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4.1 ★★★★★
Based on 8 reviews
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Product Reviews
A
Verified Purchase
Amazon Customer
Natrona Heights, 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
Pawtucket, 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
Alexandria, 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
Omaha, 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
Charlottesville, 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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