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Description
Elkay Lustertone Classic 17" Drop In/Topmount Stainless Steel ADA Kitchen Sink, Lustrous Satin, MR2 Faucet Holes, LRADQ172055MR2Elkay Lustertone Classic 17" Drop In Topmount Stainless Steel ADA Kitchen Sink, Lustrous Satin, MR2 Faucet Holes, LRADQ172055MR2 An Elkay Lustertone Classic stainless steel sink looks as great on day 3,000 as it does on day one. The rich, uniform grain is scratch resistant for heavy duty use, whether it's a kitchen sink or a prep, bar, laundry or commercial sink. Light scratches, which occur with everyday use, naturally blend into the finish of this
Elkay Lustertone Classic 17" Drop In/Topmount Stainless Steel ADA Kitchen Sink, Lustrous Satin, MR2 Faucet Holes, LRADQ172055MR2
An Elkay Lustertone Classic stainless steel sink looks as great on day 3,000 as it does on day one. The rich, uniform grain is scratch resistant for heavy-duty use, whether it's a kitchen sink or a prep, bar, laundry or commercial sink. Light scratches, which occur with everyday use, naturally blend into the finish of this durable sink with time. Deeper scratches are repairable with an Elkay stainless steel restoration kit. Available in ADA depths.
Available In:
Please see our color disclaimer.
Features
- ADA COMPLIANT: Product is ADA compliant when properly installed
- REPAIRABLE FINISH: Finish is scratch resistant to heavy-duty use. Deep scratches are repairable. Lustrous grain reflects light evenly for high shine
- DROP-IN INSTALLATION: Sink is designed for drop-in installation to make the sink a focal point of your room
- SINGLE BOWL: Bowl gives you uninterrupted space for washing and stacking dishes or other household tasks
- 18-GAUGE STAINLESS STEEL: Highest quality 18-gauge thickness and Type 304 stainless steel for lasting durability, performance and lustrous beauty
- Sound Guard TECHNOLOGY: Large pads enhance sound-deadening performance for a quieter time at the sink
- QUICK-CLIP MOUNTING SYSTEM: Quickly and securely install sink from above the counter with a series of clips that attach to laminate countertops.
- OFFSET DRAIN: Drain placement provides more usable space on the bottom of the sink and in the cabinet below
- MADE IN THE USA: This Elkay product is proudly made in the USA
- DRAIN OPENING: Sink drain opening measures 3-1/2"
- BASE CABINET: Recommended Minimum Base Cabinet Size: 21"
- California residents see Prop 65 Warnings.
Details
| ADA Compliant?: | Yes |
|---|---|
| Bowl Shape(s): | Rectangle |
| Bowl Split: | Single |
| Box Height: | 23.19" |
| Box Length: | 23.31" |
| Box Weight: | 12 lb(s) |
| Box Width: | 8.94" |
| Code / Standard Compliance: | NPCC |
| Collection: | Lustertone Classic |
| Color: | Lustrous Satin |
| Country of Origin: | USA |
| Cutout Dimension: | 16-3/8" x 19-3/8" (416mm x 492mm) with 1-1/2" (38mm) corner radius |
| Drain Size: | 3.5 |
| Finish: | Lustrous Satin |
| Freight Class (LTL Only): | 250 |
| Gauge: | 18 |
| Harmonized System Code: | 7324100010 |
| Inner Depth: | 5.5" |
| Inside Bowl Dimensions: | 14" x 14" x 5.375" |
| Installation Type: | Drop In/Topmount |
| Item Height: | 5.5" |
| Item Length (Front to Back): | 20" |
| Item Weight: | 8 lb(s) |
| Item Width (Side to Side): | 17" |
| Made In USA?: | Yes |
| Material: | Stainless Steel |
| Minimum Cabinet Size: | 21" |
| Mounting Hardware Included: | Included for 3/4" (19mm) countertop |
| Number of Bowls: | 1 |
| Number of Faucet Holes: | MR2 |
| SKU: | LRADQ172055MR2 |
| Shape: | Rectangle |
| Sound Deadening: | Bottom only pads |
| Style: | Traditional |
| cUPC Certified?: | Yes |
Warranty
Elkay Warranty Details (PDF)
Installation Instructions
Installation Instructions 1 (PDF)
Product Specifications (PDF)
Product Care
Elkay Product Care (PDF)
Video(s)
Product Video 1
Product Video 2
Product Video 3
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keywords, QUICK CLIP, rear drain, offset drain, off set drain
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- Free Standard Shipping on $100+ Orders to the USA.
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4.1 ★★★★★
Based on 16 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018
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