cute short white dress White Lace A Line Cap Sleeves Short Homecoming Dress with Open Back US18W / As Picture
SKU: 50959768013
cute short white dress

cute short white dress White Lace A Line Cap Sleeves Short Homecoming Dress with Open Back US18W / As Picture

$20.95 $23.28
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No.2 Bestseller on beerandwine.guide >
Size: 4
Size Recommendation How To Measure

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Ships within 48 hours · Estimated delivery Jun 30 - Jul 5

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Description

cute short white dress White Lace A Line Cap Sleeves Short Homecoming Dress with Open Back US18W / As PictureWelcome to our store! We are shipping worldwide by international express, and offer free custom made service! Dress details: 1. Material: Satin, Lace 2. Color: As Picture(For custom color, please check Satin Color Chart) 3. Closure: Zipper 4. Length: Knee length. 5. Sleeve: Cap sleeves 6. Embellishment: Lace appliques About Size: Standard size or custom size, for custom size, we need the following measurements, you can fill in your measurements in the

Welcome to our store! We are shipping worldwide by international express, and offer free custom made service!

Dress details: 
1. Material: Satin, Lace

2. Color: As Picture(For custom color, please check Satin Color Chart)

3. Closure: Zipper

4. Length: Knee length.

5. Sleeve: Cap sleeves

6. Embellishment: Lace appliques

About Size: 
Standard size or custom size, for custom size, we need the following measurements, you can fill in your measurements in the order note at shopping cart, or you can send it to us directly by email. Please refer to the below measuring guide to ensure you get the correct measurements (we recommend you to find a local tailorshop to get your measurements).

Custom measurements we need:

1. Bust=______ cm/inch 
2. Waist=______cm/inch 
3. Hip=_______cm/inch 
4. Shoulder to shoulder from back=_______cm/inch
5. Nipple to nipple=_______cm/inch
6. Shoulder to nipple=_______cm/inch 
7. Shoulder to Waist=_______cm/inch 
8. Height from top head to toe=_______cm/inch

For long sleeves dress, we also need:
1. Arm length=_______cm/inch
2. Arm hole (arm eye circumference) =_______cm/inch
3. Wrist hole=__________cm/inch

More information:

1. Delivery time: 
1). Normal order: 18 to 25 days.
2). Rush order: within 15 days, but there will be an accelerating fee of US$28.00.

2. Packing: each dress will be packed tightly with waterproof bag (delicate box package or plastic bags).

3. Shipping Method: by UPS, FedEx, DHL or some special airlines.

4. Payment method: PayPal and Credit Card.

Custom taxes:

Except for Unite States, most countries charge customs taxes, in order to save cost for you, we have to remark around $30-$40.00 on the invoice, then you just need to pay a small amount of taxes, please note that it's express companies help Customs to collect this charges, it is not shipping cost, as shipping cost has already paid before shipping.

Advantages of Promnova:

We’ve been supplying dresses for some famous brands and we also make dresses for designers in European and USA for many years. Here we have mature industry of woman dresses, experienced and talented tailors with advanced sewing tech, complete and strict quality control system. All of our dresses are handmade with imported high quality fabrics, we support free custom made dresses and worldwide shipping! Our strong team will use every ounce of our heart and soul to make each dress be your dream dress!

Buying Tips:

Thanks so much for your shopping, we will be very appreciated if you can leave us a FIVE STAR positive review if you are satisfied with our services and dress. For any slight chance you received an unsatisfied item, please don't worry and contact us immediately, we will try our best to help. 

Happy shopping here!

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. 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
SKU: 50959768013

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4.8 ★★★★★
Based on 800 reviews
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Product Reviews
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Verified Purchase
William P Ross
Los Angeles, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Whiting, 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
M
Verified Purchase
mackster
Draper, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Los Angeles, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 25, 2018

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