SKU: 64262227734
thin wood trellis

thin wood trellis Natural Cedar A-Frame Trellises

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Description

thin wood trellis Natural Cedar A-Frame TrellisesMade in The USA Available exclusively at Eartheasy Transform your vegetable patch into a lush oasis with these attractive A frame trellises! Made in the USA from durable cedar, A frame trellises provide sturdy support for vining vegetables such as cucumbers, beans and peas, allowing plants to grow vertically, saving space and maximizing yields. Read more Built to last season after season, these solid cedar trellises are sturdy enough to support

Made in The USA - Available exclusively at Eartheasy

Transform your vegetable patch into a lush oasis with these attractive A-frame trellises! Made in the USA from durable cedar, A-frame trellises provide sturdy support for vining vegetables such as cucumbers, beans and peas, allowing plants to grow vertically, saving space and maximizing yields.

Read more

Built to last season after season, these solid cedar trellises are sturdy enough to support cucumbers, squash, and other vining vegetables. As plants naturally climb and sprawl, an A-frame trellis offers a strong frame for plants to latch onto while growing upwards. The vertical and horizontal cross pieces are 1" thick with rounded edges to support heavier plants without cutting into the stems.

A-frame garden trellises can be installed on top of a raised garden bed or directly on the ground.

Features & Benefits:

  • Sturdy A-frame design: Provides robust support for climbing vegetables to grow vertically, including heavier vegetables such as cucumbers, squash and gourds.
  • Solid cedar construction: Unique tongue and groove assembly is strong and lightweight, superior to other wood trellises. Dadoed crosspieces fit together to form a tight grid, we have found stronger than traditional lattices.
  • Enhances garden design: A-frame trellises create a visually appealing focal point in your garden, showcasing the vertical growth of plants.
  • Improved airflow & plant health: Elevating plants off the ground promotes better air circulation, reducing risk of diseases and minimizing contact with pests and soil-borne diseases, for healthier plants and harvests.
  • Flexibility and portability: A-frame trellises are versatile and easy to move or reposition as needed, providing flexibility in garden design and planting.
  • Made in the USA

Size Options:

Choose from three sizes to suit your specific gardening needs and space. Each cedar A-frame trellis has two panels. Dimensions below are the width and height of each side.

  • Small: 30" W x 42" H
  • Medium: 42" W x 48" H
  • Large: 42" W x 60" H
  • X-Large: 60" W x 60" H

When ordering an A-frame garden trellis for one of our Natural Cedar Raised Garden Beds, we recommend the small or medium trellis for a 3 ft. wide bed. The medium and large trellises fit perfectly on 4 ft. wide beds. And the X-Large trellis sits directly on the ground, providing a sturdy, freestanding structure ideal for supporting climbing plants and spreading vines.

The space between the trellis cross pieces (squares) is 4.75" x 4.75" on all sizes.

Assembly:

Straightforward assembly with just a power drill. Trellis kits come with detailed instructions, hardware and wood glue. Assembly involves arranging the vertical and horizontal cross pieces on a flat surface, then gluing the notches and applying pressure to form a grid. Once the cross pieces are in place, the frame can be attached with included screws, again applying wood glue to the notches where the cross pieces join the frame.

The top frame piece where the two trellis panels connect (the top of the "A") has two hinges pre-attached. The hinges allow the angle of the trellis to be adjusted, or folded flat for storage.

Trellises can be used on the ground inside raised beds, or for better height and stability, set on top of your raised bed. L-brackets are included to attach the inside edges of the A-frame to the raised bed.

These cedar trellises can be painted or stained, or left unfinished to gray naturally. We offer Eco Wood Treatment as one option. Eco Wood Treatment works by penetrating wood fibers to create a natural buffer against water and fungal decay. It increases wood's durability while creating an aged, silvery patina after just a few hours.

We recommend letting the boards sit in a garage, shed or covered area for 3-5 days to allow the boards to acclimatize to their new environment. This helps to prevent any cupping, warping, and gapping.

Shipping Notes
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Exchange/Return Notes
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  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
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SKU: 64262227734

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4.2 ★★★★★
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William P Ross
Fort Morgan, 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.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Grantham, 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
Lexington, 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
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mackster
Birmingham, 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
S
Verified Purchase
Stergios Papadimitriou
Battle Creek, 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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