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Description
Meritt 37" Total Height Brown Leather Seat Black Metal Frame StoolOverview Featuring a medium brown colored, high quality leather seat, the Meritt II is a counter height stool that is stylish and comfortable. The leather seat flaunts a horizontal stitch to it which adds a touch of sophisticated appeal to the design. The intricately designed, uber comfortable leather seat is mounted on a matte black toned iron frame which guarantees stability. The metal frame on the Meritt II also serves as a footrest which adds to
Overview
Featuring a medium brown colored, high-quality leather seat, the Meritt II is a counter height stool that is stylish and comfortable. The leather seat flaunts a horizontal stitch to it which adds a touch of sophisticated appeal to the design. The intricately designed, uber comfortable leather seat is mounted on a matte-black toned iron frame which guarantees stability. The metal frame on the Meritt II also serves as a footrest which adds to the functionality factor of the piece. A combination of leather and metal accents, unparalleled comfort and stunning visuals make the Meritt II a highly recommended piece for spaces based on the Industrial or Mercana Modern style of design.
Features
- 37" Total Height Bar Stool: With a seat height of 25" tall, the Meritt II measures 23.0" wide by 37.0" high.
- Brown Leather Wrap: The Meritt II flaunts a superior, brown leather wrap with a horizontal stitch detail and an iron base finished in matte-black, adding to the visuals of the ice.
- Sturdy Iron Frame: Featuring a frame and base crafted from iron, the Meritt II is a sturdy, high back bar stool with exceptional stability and balance.
- Footrest: The four legs of the Meritt II are stabilized by metal bracing that also serves as a footrest and ensures a comfortable seating experience.
- Industrial Design: Prudently designed to suit industrial spaces, the Meritt II flaunts a high-back seat wrapped with brown leather which complements its iron frame.
- No Assembly Required: Delivered pre-assembled, the Meritt II is ready to turn heads in your space as soon as it arrives at your doorstep.
Specifications
- Adjustable Shelves?: No
- Hand Made (Y/N): Yes
- Hand Painted (Y/N): No
- Scratch Resistant (Y/N): No
- Stain Resistant (Y/N): Yes
- Water Resistant (Y/N): Yes
- Fire Resistant (Y/N): No
- Finished Back (Y/N): Yes
- Kiln-Dried Wood: No
- Unfinished: No
- Leg/Base Type: Sled
- Shagreen-embossed Exterior: No
- Seat Back Type: Full Back
- Arms Included?: No
- Upholstered: Yes
- Tufted Cushions: No
- Nailhead Trim?: No
- Swivel: No
- Foldable: No
- Stackable?: No
- Adjustable Height (Y/N): No
- Pneumatic/Hydraulic Lift?: No
- Footrest Included?: Yes
- Seating Capacity: 1
- Seat Cushion Reversible Cushion (Y/N): No
- Performance Fabric (Y/N): No
- Back Included?: Yes
- Plug-In: No
- Made to Order: No
- Main Material: Metal
- Material Code - Body: M0449-BLK-M
- Body Material Type: Metal
- Main Color: Brown
- Body Metal Type: Iron
- Body Color: Black
- Body Natural Variation: No Natural Variation
- Fabric/Leather Material Code - Back: Local Brown
- Back Material Type: Leather
- Back Fabric/Leather Composition: 100% Leather
- Back Fabric/Leather Color Fastness: 4
- Back Genuine Leather Type: Top Grain Leather
- Back Genuine Leather Animal Type: Cow
- Back Color: Brown
- Back Natural Variation: Natural Leather Color & Texture Variation
- Back Foam Density: 32+_1
- Material Code - Legs/Base: M0449-BLK-M
- Leg/Base Material Type: Metal
- Leg/Base Metal Type: Iron
- Leg/Base Color: Black
- Leg/Base Natural Variation: No Natural Variation
- Fabric/Leather Material Code - Seat: Local Brown
- Seat Material Type: Leather
- Seat Fabric/Leather Composition: 100% Leather
- Seat Fabric/Leather Color Fastness: 4
- Seat Genuine Leather Type: Top Grain Leather
- Seat Genuine Leather Animal Type: Cow
- Seat Finish Application Description: Other
- Seat Finish Texture: Other
- Seat Color: Brown
- Seat Natural Variation: Natural Leather Color & Texture Variation
- Seat Foam Density: 32+_1
- Purposeful Distressing Type: No Distressing
- Reclaimed Wood?: No
- Upholstery Material: Genuine Leather
- Upholstery Fill Material: Foam
- Frame Material: Metal
- Overall Weight Capacity: 200 lbs (90.7 kg)
- Seat Height - Floor to Seat: Counter Height (23"-27")
- Seat Width - Side to Side: 17.25 inch (43.82 cm)
- Back Height: NaN inch (NaN cm)
- Seat Depth - Front to Back: 15.5 inch (39.37 cm)
- Arm Depth - Front to Back: Does Not Apply
- Arm Width - Side to Side: Does Not Apply
- Assembly Required (Y/N): No
- Removeable Legs (Y/N): No
- Additional tools needed (Y/N): No
- Avoid Power Tools?: Yes
- Level of Assembly: None
- Prop 65 Label: Yes
- Prop 65 Chemicals: Formaldehyde (gas)
- Supplier Intended and Approved Use: Residential Use
- Uniform Packaging and Labeling Regulations (UPLR) Compliant: No
- Canada Product Restriction: No
- Reason for Restriction: Does Not Apply
- General Certificate of Conformity (GCC): Yes
- Composite Wood Product (CWP): No
- Commercial Warranty: No
- Warranty Length: 90 Days
- Product Warranty: Yes
- Eligible for Hardware Packs: No
- Floor Protection: Plastic Glides
- Eligible for Replacement Parts: No
- Design Style: Urban Loft, Industrial, Relaxed Modern
- Mercana Style - Influences: Bold & Eclectic Modern, Sleek & Chic Modern, Industrial
Dimensions
- Product Size (inches): 17.7L x 21.7W x 37.8H
- Weight: 24.50 lbs
- Box Size (inches): 18.1L x 24.0W x 39.0H
- Box Weight: 30.60 lbs
- CBM: 0.2379
Additional Resources
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- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
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4.3 ★★★★★
Based on 26 reviews
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Product Reviews
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
★★★★★ 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