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Description
East West Furniture Dublin 5 Piece Modern Set Includes a Round Wooden Table with Dropleaf and 4 Kitchen Dining Chairs, 42x42 Inch, OakAbout This MODERN DINING TABLE SET: This dining table set includes a dining table and 4 kitchen chairs that can enhance the aesthetic appeal of any household. The versatile piece features a chic design that can seamlessly blend in with almost any existing decor. Add a touch of sophistication and charm to your dining area with our dining table set, which boasts of an attractive and elegant style. MODERN DINING TABLE SET FINISH: The modern dining table
About This
- MODERN DINING TABLE SET: This dining table set includes a dining table and 4 kitchen chairs that can enhance the aesthetic appeal of any household. The versatile piece features a chic design that can seamlessly blend in with almost any existing decor. Add a touch of sophistication and charm to your dining area with our dining table set, which boasts of an attractive and elegant style.
- MODERN DINING TABLE SET FINISH: The modern dining table features an Oak-colored table top and Oak pedestal legs, while the dinner chairs have four Oak legs and a wooden seat. The attractive design of the wooden chairs and beautiful modern kitchen table allows them to complement any room decor. The kitchen table set has been designed with a high-quality combination of colors, giving it a refined appearance that adds a touch of elegance to any home.
- TOP QUALITY MATERIAL: Constructed from high-quality Asian wood, commonly known as Rubber Wood, our dining table set offers exceptional steadiness and durability for long-term daily use. Asian wood has long been a popular choice of wood for its toughness, strength, and durability. The outstanding properties of Asian Wood ensure that our dining set can withstand frequent use over an extended period without compromising its stability and durability.
- MODERN CHAIR SEAT MATERIAL: Our modern kitchen chairs are furnished with wooden seats that augment the aesthetic appeal of your kitchen. The wide seats provide added comfort, and the slatted back design of our breakfast chairs is adaptable to various decor styles, from rustic to modern. Furthermore, the smooth wooden seat guarantees customer comfort, contributing to an enjoyable dining experience.
- ITEM DIMENSIONS: The Dimensions of a Wooden Chair are: Length: 21; Width: 18; Height: 37. Dimensions of the Dinner Table are: Length: 42; Width: 42; Height: 29.5.
- This Dining Table Set comes in 4 boxes. One box for the table top, one for the pedestal and 2 boxes for the chairs.
Overview
- Brand : East West Furniture
- Color : Oak
- Room Type : Kitchen
- Included Components : Table and 4 Chairs
- Size : 42x42 Inch
- Material Hardwood/Wood Seat : Style DLGR5-OAK-W
- : Material Hardwood/Wood Seat
- : Style DLGR5-OAK-W
- Shape Round : Item Weight 125 Pounds
- : Shape Round
- : Item Weight 125 Pounds
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4.3 ★★★★★
Based on 24 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