SKU: 44296959216
lightweight plant pot

lightweight plant pot Cloche Extra Large Planter

$19.44 $21.60
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Description

lightweight plant pot Cloche Extra Large PlanterSpecifications Available in 3 colors, each sold separately Product Dimensions: 21. 5"W x 20"H Base: 16"W Weight: 44 lbs. Inside Dimensions: Top: 20. 25"W Bottom: 15"W Height: 19. 5" Available in Brown, Grey and Charcoal finishes Makes a beautiful focal point for your garden setting, either filled with beautiful blooms or greens or left empty as a wonderful accent piece Manufactured with a high density yet lightweight concrete cast stone mix Finish

Specifications
  • Available in 3 colors, each sold separately
  • Product Dimensions: 21.5"W x 20"H / Base: 16"W / Weight: 44 lbs.
  • Inside Dimensions: Top: 20.25"W / Bottom: 15"W / Height: 19.5"
  • Available in Brown, Grey and Charcoal finishes
  • Makes a beautiful focal point for your garden setting, either filled with beautiful blooms or greens or left empty as a wonderful accent piece
  • Manufactured with a high density yet lightweight concrete cast stone mix
  • Finish/Stain is hand applied by trained artisans
  • Features unique designer detail and superior craftsmanship
  • Weathers naturally outdoors
  • One drainage hole, centered, approximately 1/2"
  • Made in the USA

Due to the nature of concrete, there is very little that can go wrong with it as long as it's properly cared for. For this reason, Campania does not offer a warranty for their products. Our 15+ years of experience has shown that Campania's products are more than capable of withstanding the test of time. These products are designed to last well over 40 years, and will as long as proper care is provided for them.

In the winter, if a planter is to remain planted, it should be raised up off the ground making sure not to block the drainage hole. This allows the planter to drain and will prevent the planter from freezing to the ground. If the planter is not to be planted, it should be brought inside a garage or shed. If this is not possible, follow the steps above, but turn the planter upside down to prevent it from filling with snow and ice. It should then be covered or wrapped with burlap or any absorbent material (old blanket/towel) and then wrapped with dark plastic to prevent it from accumulating moisture.

    Description

    Lightweight Concrete

    Campania proudly manufactures its cast stone products in the USA using locally sourced materials. All of our cast stone pieces are manufactured using the wet casting technique with a proprietary high density cast stone mix, yielding a PSI strength of approximately 7,500. Our cast stone products may qualify for LEED credits as applicable to your project. Cast stone produces a strong and durable product that will weather naturally in an outdoor environment. Simply stated, cast stone is chosen for its beauty and authenticity. Our material is natural, authentic, made in the USA, and 100% recyclable. These planters are often specified for hospitality projects, large scale public space installations, pedestrian and traffic safety, and other custom projects. Although we recommend that you take advantage of the quick turnaround of our stocked product; these shapes, finishes, and/or sizes are not always right for every job. Campania has the capability to produce custom molds and colors in our Pennsburg, PA factory.

    Campania is pleased to introduce a new collection of lightweight poured glass fiber reinforced concrete manufactured at our facility in Pennsburg, Pennsylvania. Unlike our cast stone, glass fibers replace stone aggregate to create a strong and durable product with a thinner profile and lighter weight.

    Shipping Notes
    • Free Standard Shipping on $100+ Orders to the USA.
    • Except Preorder products are shipped in 48 hours.
    • Delivery to the USA:
    1. Standard Shipping : 3-10 business days
    • If time is of the essence, please consider selecting expedited delivery for faster service.
    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]
    • Please click here for more details>>> Return & Exchange Policy
    SKU: 44296959216

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    4.1 ★★★★★
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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.
    Lexington, 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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    Verified Purchase
    Par
    Lowell, 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
    Los Angeles, 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.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on February 26, 2022
    A
    Verified Purchase
    Amazon Customer
    Draper, 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

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