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Description
formal evening dress for women Ladivine CC446 Dress SMOKY BLUE / 18The contrasting long formal a line beaded evening dress by LaDivine CC446 is a high density bead bodice, combined with the lightness of a chiffon lined skirt. The design is based on an organised V neckline which moves to 3 4 length sleeves, giving the body a lot of coverage but leaving the upper body with a clean open frame. This firm, adorned top, coupled with the humble sleeve length, makes the garment an effective and trustworthy purchase both in a
The contrasting long formal a-line beaded evening dress by LaDivine CC446 is a high-density bead bodice, combined with the lightness of a chiffon-lined skirt. The design is based on an organised V-neckline which moves to 3/4 length sleeves, giving the body a lot of coverage but leaving the upper body with a clean open frame. This firm, adorned top, coupled with the humble sleeve length, makes the garment an effective and trustworthy purchase both in a formal position, say the mother of the bride or groom.
The gown flares into an A-line skirt that goes all the way to the floor, made out of quality chiffon, at the natural waistline. This material is chosen due to its smooth surface and the capability to flow like long, straight lines that will naturally move with the user. This is completely lined so that the interior of the skirt remains opaque, giving a smooth fit with the skin. Offered in an elegant shade of soft and dark colors such as English Violet and Indigo Grey, this one is made to appeal to those who want a high-structure formal appearance, but that is down-to-earth and practical.
Key Features:
- Detailed Beaded Bodice: A high-caliber embellished top that provides a constant shimmer and a firm, structured fit.
- 3/4 Length Sleeves: Offer a balanced amount of coverage for the arms while supporting the overall vertical line of the dress.
- Fluid Chiffon Skirt: A lightweight, floor-length A-line cut that provides a smooth drape and unrestricted mobility.
- Structured V-Neckline: Delivers a sharp, vertical frame that balances the intricate patterns of the beadwork.
- Full Interior Lining: Ensures the gown maintains its shape and provides a professional-quality, opaque finish.
Available Colors:
Emerald, English Violet, Indigo Grey, Mauve, Peacock, Smoky Blue
Perfect for Special Occasions!
A solid selection for mothers of the bride, wedding guests, and formal evening ceremonies for those seeking a gown that balances heavy bodice embellishment with a lightweight, movable skirt.
Details:
- Silhouette & Fit: A-Line
- Length: Full Length
- Fabric: Beaded Mesh and Chiffon
- Sleeve Style: 3/4 Sleeve
- Back: Zipper closure
- Sizes: 6, 8, 10, 12, 14, 16, 18, 20
- Designer: LaDivine
- Style: CDCC446
- Occasion: Formal, Wedding Guest, Mother of the Bride
Garment Care & Handling:
- Professional Cleaning Only: Specialized dry cleaning is required to protect the beaded bodice and prevent the chiffon from water spotting.
- Store on a Padded Hanger: Utilize internal hanging loops to distribute the weight; do not hang solely by the shoulders to prevent the weight of the beads from stretching the fabric.
- Steam with Caution: Use a low-heat garment steamer on the chiffon skirt. Avoid direct contact with the beaded bodice, as heat can affect the integrity of the embellishments.
- Storage: Keep the dress in a breathable fabric garment bag to protect the delicate chiffon from being snagged by the beaded section or other garments.
Shipping & Delivery:
- Orders are prioritized for rapid fulfillment, with most packages prepared within 48 to 72 hours.
- Reliable ground shipping is provided with a standard arrival window of 2 to 7 business days.
- Real-time tracking updates are sent via email the moment your dress is scanned for departure.
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]
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4.1 ★★★★★
Based on 2018 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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