SKU: 23844398731

PVC Tablecloth Protector Table Cover Dining Table Cloth Plastic 2134x1070mm 1.5mm

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Description

PVC Tablecloth Protector Table Cover Dining Table Cloth Plastic 2134x1070mm 1.5mmIf you're looking for the perfect clear plastic tablecloth to keep your table clean and free of stains then this is the solution for you. Made from an easy to clean phthalate free plastic, the Randy and Travis Machinery Tablecloth Protector is a great solution for those occasions when you want your kitchen table to stay looking immaculate, even if there's the chance that food is going to get everywhere. A fantastic option for events such as children's

If you're looking for the perfect clear plastic tablecloth to keep your table clean and free of stains then this is the solution for you. Made from an easy to clean phthalate-free plastic, the Randy and Travis Machinery Tablecloth Protector is a great solution for those occasions when you want your kitchen table to stay looking immaculate, even if there's the chance that food is going to get everywhere. A fantastic option for events such as children's birthday parties and other special occasions where the table can get a bit messy or covered in food. This clear plastic tablecloth can also be utilised for a number of other surfaces and uses due to its size and ease of use. It's a great option if you need a clear plastic covering for the ground or other work surfaces and with the spare edging, you can cut it down to a specific size or shape.

Features and Specifications:

  • Size: 2134mm x 1070mm
  • Thickness 1.5mm
  • Weight 4.5kg
  • Material: PVC
  • PVC material offers benefits including lightweight, waterproof, moisture-proof, flame-retardant, heat-insulating, wear-resistant, dry-resistant, easy to clean and preserve
  • A DIY design that allows you to tailor the tablecloth size to the surface or table you are using it for
  • Large size and wide design allows it to fit a variety of surfaces including dining tables, wooden tables, glass, marble, stainless steel tops, office desktops, writing desktops, coffee & end tables, TV bench, dressing tables, nightstand, countertop, hard floor etc
  • Easy to clean and reusable design allows you to wipe off the plastic tablecloth protectors and easily store them away for future use


 

Note:
COM (change of mind) Return Policy:
The product must be unopened and in brand new condition, with the return to be organised by the customer. Once received and verified to be in the condition specified, refund is only of the item value minus 15% restocking fee (not including initial shipping). An additional return charge, which is same cost as that of initial shipping, will be deducted from the refund on returns due to delivery failure caused by customer error. For the health and safety of our staff and other customers, we are unable to accept returns of personal use items.

Shipping Notes
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SKU: 23844398731

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4.1 ★★★★★
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Verified Purchase
Adam
Lowell, 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
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Amazon Customer
West Palm Beach, 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
Los Angeles, 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.
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Reviewed in the United States on May 15, 2018
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Verified Purchase
Stergios Papadimitriou
Massapequa, 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.
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Reviewed in the United States on August 25, 2018
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Verified Purchase
Sabrina
Omaha, US
★★★★★ 5
100% Recommend
Format: Paperback
Invincible Compendium One completely lived up to the hype. From the very first chapter, I was hooked by the story, the action, and the character development. What starts off feeling like a classic superhero story quickly becomes something much deeper, darker, and way more emotional than expected. The artwork is incredible and the fight scenes are intense without feeling repetitive. Every character feels important and layered, especially Mark and Omni-Man. The pacing is excellent for such a massive collection, and it’s hard to put down once you start reading. If you’re a fan of superhero comics but want something with real stakes, shocking twists, and strong storytelling, this is absolutely worth reading. Easily one of the best graphic novels I’ve picked up in a long time.
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Reviewed in the United States on May 23, 2026

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