dresser and chest set Nathan James Andrew Rattan Bedroom Set Light Oak
SKU: 49107572947
dresser and chest set

dresser and chest set Nathan James Andrew Rattan Bedroom Set Light Oak

$21.60 $24.00
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No.2 Bestseller on bertklapecinc.com >
Size: 4
Size Recommendation How To Measure

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

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Description

dresser and chest set Nathan James Andrew Rattan Bedroom Set Light OakAT A GLANCE Sturdy powder coated metal base and drawer pulls Interchangeable leg options so dressers can sit flush side by side Leveling floor protectors included 3 dresser drawers on strong metal drawer slides Dresser drawer interlock safety mechanism allows 1 drawer open at a time to prevent tipping hazards Natural rattan drawer fronts 11" deep nightstand drawers 90 lbs weight limit for top of nightstand 11 lbs weight limit per nightstand drawer Due

AT A GLANCE

  • Sturdy powder coated metal base and drawer pulls
  • Interchangeable leg options so dressers can sit flush side-by-side
  • Leveling floor protectors included
  • 3 dresser drawers on strong metal drawer slides
  • Dresser drawer interlock safety mechanism allows 1 drawer open at a time to prevent tipping hazards
  • Natural rattan drawer fronts
  • 11" deep nightstand drawers
  • 90 lbs weight limit for top of nightstand
  • 11 lbs weight limit per nightstand drawer
  • Due to characteristics of natural rattan, each piece is unique and products will have variations in color and texture

PRODUCT DESCRIPTION

Meet Andrew, a sturdy, stylish dresser and nightstand with a metal base and natural rattan fronts. Smooth slides, a safety lock, and leveling protectors add function, while interchangeable legs offer a custom fit. Each piece is unique, bringing warmth and texture to your space!

STYLE

Modern Contemporary

MATERIALS

  • Cane, Metal, MDF, Paper Laminate, Particleboard

ASSEMBLY & CARE

  • Estimated assembly time: 430 minutes
  • All hardware and instructions included

Care Instructions

Dust with a soft, dry cloth or duster. Clean with a soft, damp, colorfast cloth + mild soap & water. For stubborn stains, use non-abrasive, water-based cleaners only.

To protect the finish, avoid the use of chemicals, household cleaners and abrasives. If you are going to use a mild cleaner or chemical, it is recommend to first test it on a small discreet location to see how it reacts with the materials before applying it to visible areas.

Always wipe the surface in the direction of the wood grain and ensure there is no pooling of liquid at the seams.

Always use a coaster when setting any vessel containing hot liquids on any surfaces.

Blot spills immediately with a clean, damp, colorfast cloth to avoid potential staining. Soak up any remaining moisture immediately with a soft, dry, colorfast cloth to prevent potential watermarks, bubbling, etc.

Limit direct contact with heat, moisture and other elements to avoid finish damage.

To prevent potential color fading, keep out of direct sunlight.

Follow assembly instructions carefully and inspect product upon receipt and each use.

SHIPPING, RETURNS & PRODUCT GUARANTEE

About Nathan James

Nathan James designs modern furniture at an honest price. Our goal is to make your shopping experience simple, from purchase to assembly.

Shipping

All orders ship free. There is no minimum purchase required. In stock products ship next day.

Returns

We offer free returns within 100 days of purchase. There are no return fees, and returns are accepted for any reason. We arrange the pickup.

Product Guarantee

All Nathan James products include a lifetime guarantee. There is no fine print, no hassle, and no registration needed.

Customer Support

You can speak to a real person on our customer support team.

Phone, text message, or WhatsApp: 1-866-619-1004

Email: [email protected]

We have over 50,000 5-star customer reviews and are in over 1 million homes.

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: 49107572947

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4.5 ★★★★★
Based on 137 reviews
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Verified Purchase
William P Ross
Boise, 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
Lake Worth, 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
Alexandria, 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
Lowell, 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
Port Orchard, 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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