SKU: 60453084710
large silver indoor planter

large silver indoor planter Large

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Description

large silver indoor planter LargeMadeira Rectangle Planter Seamless Modern Minimalist Aluminum (Silver) Metal, Large Long Block 12" Height x 30" Long Architectural Industrial Planter Box Outdoor Indoor No Drainage Elevate your dcor and create a visually stunning display of greenery! Ultra Heavy Duty Brushed Aluminum. Durable and long lasting. Lightweight and easy to reposition. No Drainage Holes. Easily drillable. Waterproof Epoxy Lining Indoor or Outdoor use. Everywhere to make a

Madeira Rectangle Planter - Seamless Modern Minimalist Aluminum (Silver) Metal, Large Long Block 12" Height x 30" Long Architectural / Industrial Planter Box - Outdoor / Indoor - No Drainage

Elevate your décor and create a visually stunning display of greenery!

  • Ultra Heavy-Duty Brushed Aluminum. Durable and long-lasting.
  • Lightweight and easy to reposition.
  • No Drainage Holes. Easily drillable. 
  • Waterproof Epoxy Lining
  • Indoor or Outdoor use. Everywhere to make a statement. Entrances, Patio, Balcony, Commercial Spaces
  • Modern and Contemporary Rectangle Design
  • Weather and UV Resistant Finish
  • 30” L x 12” W x 12” H
  • Made in India by NMN Designs

30" Rectangular Span Aluminum Planter

The 30" Rectangular Span Planter is a streamlined solution for defining boundaries and adding structured greenery to any environment. With its elongated profile and minimalist design, this planter serves as a high-end architectural tool for creating natural partitions in lobbies, offices, and outdoor terraces.

Customizable Drainage Options

Designed as a watertight vessel, the Span Planter offers total flexibility for indoor placement, ensuring floors and delicate surfaces remain protected. For permanent outdoor installations, drainage holes can be easily drilled using a standard metal drill bit, allowing for direct planting and proper moisture management in exposed environments.

Engineered Excellence

Crafted from high-grade brushed aluminum, the Span Planter features seamless welding for a flawless, modern finish. The interior is treated with a waterproof epoxy lining, enhancing its durability and ensuring long-term performance. Lightweight and frost-resistant, it is built to maintain its structural integrity in any professional setting.


Why Choose Aluminum for Architectural and Commercial Spaces?

Aluminum is the preferred material for professional installations due to its specific performance advantages:

  • High Strength-to-Weight Ratio: This 30" trough is significantly lighter than stone or concrete, making it easy to move and install while remaining structurally rigid.

  • Natural Corrosion Resistance: Aluminum resists rust naturally. It remains unaffected by moisture, making it ideal for high-traffic outdoor areas.

  • Structural Stability: Aluminum does not warp or crack like wood or plastic. It maintains sharp, clean rectangular lines even in extreme temperature fluctuations.

  • Ease of Customization: Unlike fiberglass or stone, aluminum can be easily modified on-site—such as drilling drainage holes—without compromising the material's integrity.

Placement & Plant Suggestions

Indoor Placements

  • Reception Counter: The 30" length provides a professional buffer without obstructing sightlines.

  • Window Ledges: The 12" width fits most deep ledges, creating a living curtain for natural privacy.

  • Office Zoning: Use multiple units atop low cabinets to define work zones while maintaining an open feel.

Outdoor Placements

  • Rooftop Terraces: Being lightweight, these are ideal for weight-restricted balconies and rooftop decks.

  • Entrance Framing: Use a pair of Span planters to flank a doorway or walkway, creating a crisp, geometric entrance.

  • Patio Dividers: Align several planters to create a "low wall" that defines outdoor dining or seating areas.

  • Poolside Accents: The corrosion-resistant aluminum is ideal for poolside environments, as it will not rust from moisture exposure.

Recommended Plant Pairings

  • Snake Plants (Sansevieria): Their vertical growth complements the modern lines; perfect for indoor privacy.

  • Ornamental Grasses (e.g., Blue Fescue): Excellent for outdoor movement and texture against the smooth metal.

  • Low Boxwood Hedge: For a formal outdoor look, three small boxwoods create a clean geometric silhouette.

  • Agave or Succulents: These thrive in low-water environments and offer a high-end, low-maintenance aesthetic.

Also available with raised base for indoor use.

Madeira Large Rectangle is just one style from the Madeira Metal Planter Collection. You can add coordinating shapes to your collection.

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SKU: 60453084710

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Belleville, 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
Boise, 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
A
Verified Purchase
Amazon Customer
Carnegie, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Battle Creek, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026

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