SKU: 78965732446
large metal indoor plant pots

large metal indoor plant pots Knox Cylinder Planter with Wood Base

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Description

large metal indoor plant pots Knox Cylinder Planter with Wood BaseKnox White Cylinder Planter Organic Modern Indoor Steel Plant Pot, Architectural Metal with Hardwood Base. Fiberglass Lining, No Drainage for Indoor Use Elevate your space with a touch of modern and contemporary elegance. Available in Two Colors: White and Brushed Stainless Steel Finishes Ultra Heavy Duty 18 8 Stainless Steel Rolled steel edges for a touch of elegance and upscale finish. Beautiful Wood Base with Walnut Finish (made in U. S. A.)

Knox White Cylinder Planter -Organic Modern Indoor Steel Plant Pot, Architectural Metal with Hardwood Base. Fiberglass Lining, No Drainage -for Indoor Use

Elevate your space with a touch of modern and contemporary elegance.

  • Available in Two Colors: White and Brushed Stainless Steel Finishes
  • Ultra Heavy-Duty 18/8 Stainless Steel
  • Rolled steel edges for a touch of elegance and upscale finish.
  • Beautiful Wood Base with Walnut Finish (made in U.S.A.)
  • Durable Waterproof Fiberglass Lining. Sturdy Base.
  • Great for Indoor Plants. 
  • UV Resistant. 
  • Contemporary Round Planter Pot Styling. Modern and Architectural.
  • For All Types of Plants and Small Trees
  • For Home and Commercial Settings (Commercial Grade)
  • Can be Drilled for Drainage Holes, or use Pot in a Pot technique.
  • Small Pot - 15.5" Tall x 12” Diameter 
  • Medium Pot - 22.5” Tall x 18" Diameter
  • Made in India (pot) U.S.A. (wood base)

Introducing the Knox Cylinder Planter in White, a Stylish Modern Round Stainless Steel Metal Planter – the ultimate addition to elevate your indoor space with a touch of contemporary elegance. Expertly crafted from high-quality, rust-resistant stainless steel, this planter is both durable and stylish, providing the perfect home for your favorite plants, greenery, and even small trees.

The hardwood base enhances the appeal of the planter. This robust and stylish base adds an extra layer of sophistication by juxtaposing the sleekness of stainless steel with the warmth and natural texture of hardwood. Specifically designed for indoor use, the finely finished base brings an added touch of elegance and charm to your interior space, ensuring your planter remains a statement piece for years to come.

Choose from two stunning finishes, brushed stainless steel or a crisp, clean white allowing you to seamlessly blend it into your existing decor.

Two Sizes. 15" Ht. and 22" Ht. Metal Planter Including Base

Available in two versatile sizes, from a compact 12-inch round (15.5" tall) or a medium 18-inch round (22.5" tall), these sleek stainless steel metal planters cater to the needs of any space, in fine homes or commercial settings. These sizes allow you to accommodate a variety of plants, allowing you to mix and match for the perfect arrangement.

White Indoor Metal Stainless Steel Metal Pot for Plants. 

The stainless steel metal planters with wood base are suitable for indoor use, offering you the flexibility to enhance your living room or office with a touch of modern charm. The sleek and polished design of the planter effortlessly complements any decor style.

To accommodate your specific drainage preferences, these planters can be drilled for drainage holes, allowing for plant health and longevity. 

Available in 2 cylinder sizes to fit plants from small plants and greenery to tall and large plants.

  • Available in Two Colors: White and Brushed Stainless Steel Finishes
  • Ultra Heavy-Duty 18/8 Stainless Steel
  • Rolled steel edges for a touch of elegance and upscale finish.
  • Beautiful Wood Base with Walnut Finish (made in U.S.A.)
  • Durable Waterproof Fiberglass Lining. Sturdy Base.
  • Great for Indoor Plants. 
  • UV Resistant. 
  • Contemporary Round Planter Pot Styling. Modern and Architectural.
  • For All Types of Plants and Small Trees
  • For Home and Commercial Settings (Commercial Grade)
  • Can be Drilled for Drainage Holes, or use Pot in a Pot technique.
  • Small Pot - 15.5" Tall x 12” Diameter 
  • Medium Pot - 22.5” Tall x 18" Diameter
  • Made in India (pot) U.S.A. (wood base)

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Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
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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]
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SKU: 78965732446

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4.4 ★★★★★
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Hashi Hanta
New York, US
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
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Carol
Natrona Heights, US
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Houston, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Par
Port Orchard, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
New York, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022

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