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beach themed wedding dresses Bohemian Wedding Dress A Line Beach Style TBW36 – TANYA BRIDALWedding Dresses Wedding Guest Dresses Special Occasion Dresses Wedding Accessories Produce Time 25 working days Rush order 15 working days Shipping Methord DHL Fedex Ups TNT Epacket Post air mail Other Shipping Time 3 10 working days by DHL Fedex Ups TNT, 15 35 working days by epacket or post air mail other Seller Email tanyanini@126. com Tel 86 18626150290 Neckline: SCOOP Sleeve Length(cm): None Wedding Dress Fabric: Silk Chiffon Train: Sweep Brush
| Wedding Dresses | Wedding Guest Dresses | Special Occasion Dresses | Wedding Accessories |
|
Produce Time |
25 working days |
| Rush order | 15 working days |
| Shipping Methord | DHL/Fedex/Ups/TNT/Epacket/ Post air mail/Other |
| Shipping Time | 3-10 working days by DHL/Fedex/Ups/TNT, 15 -35 working days by epacket or post air mail /other |
| Seller Email | [email protected] |
| Tel | 86-18626150290 |
- Neckline: SCOOP
- Sleeve Length(cm): None
- Wedding Dress Fabric: Silk Chiffon
- Train: Sweep/ Brush Train
- For Pregnant Women: Yes
- Dresses Length: Floor-Length
- Back Design: BZipper
- Item Type: Wedding Dresses
- Sleeve Style: REGULAR
- Built-in Bra: Yes
- is_customized: Yes
- Decoration: Appliques
- Decoration: Lace
- Fashion Element: vintage
- Model Number: TBW35
- Waistline: Natural
- Silhouette: A Line
- Size: Custom Size or US Size
- Style: Elegant Wedding Dress
- Color: Ivory or Custom Color
- plus size >16: extra fee $30
- Accessories: No
- We Would like use Strong Marterial,the fabric is soft and looks awesome, Dry clean or cold water hand wash
- For standard size dress. we would come out based on our standard size table, before you order, please make sure the detail measurement matched the size you need. System default size is based on US size.If you need to customize size，please feel free to contact us.
Size Chart:
Buyer can choose size according to the size chart below
If size not fit to you ,you can choose custom made ,but please contact with us first .Thank you !
if you need custom made ,please give us your size according to this guide .
Color Chart:
Buyer can choose any color from the color Chart Below :
Shipping:
After your payment ,we will ship your dress out within 15 working days .
Before we ship dress out ,we will confirm the dress photos with you .
Usually we choose DHL ,UPS,Fedex,Epacket to shipping your dress out according to your country policy .
Also we can send to you the tracking number after we ship goods out .
Notes:
1.The item will be sent to your address,please make sure the address is correct and please let me know your contact name (Full name) and your phone Number
2.The dress does not incloud any accessories such as :wedding veils ,gloves or petticoat.
3.If you are concerned about the return policy before placing the order,please read our return policy carefully at the bottom of page.
4.the taxs are charged by your country,so we will do not cars of them,but if you have suggestion,we will try our best to lower down such cases,thank you for your co-operations and standing .
Enjoy your perchase.
Refund policy:
Please confirm your order (right size, color, style) carefully before you decide to place the order. All the orders are arranged according to your order confirmation.
As always, if there is a problem or if the item is unsatisfactory, please do contact us first for a quick and satisfactory resolution, such as, refund or exchange another new item for you.
Please kindly contact us for the return at first within 48 hours after receiving the item.
The returned item must be in perfect condition, as it was sent to you, has not been altered and has not been worn. If there is any dust, dirty spots, change and so on, we shall not offer refund.
We offer FREE REPAIR on your dress! But the postage to send it back and re-shipping cost to you will be both on your account.
It is required that the item or dresses should be returned to us within 14 days after the return request is accepted.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- 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
4.8 ★★★★★
Based on 426 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on August 25, 2018
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