SKU: 52342344640
air lily plant

air lily plant Peace Lily House Plant | Natural Indoor Air Purifier

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Description

air lily plant Peace Lily House Plant | Natural Indoor Air PurifierIntroduction On NASA's Top 10 Air Cleaning Plants! Natural Air Purifier Removes Toxins from your home Symbol of Hope, Peace, Prosperity, Tranquility, and Purification A Lovely Gift for Sympathy, Get Well, Apology, etc Low Maintenance House Plant Description The botanical name of the Peace Lily, Spathiphyllum, means peace and prosperity. What a perfect name for this beautiful plant. The Peace Lily is a symbol of hope, tranquility, rebirth, and

Introduction

On NASA's Top 10 Air Cleaning Plants!

  • Natural Air Purifier - Removes Toxins from your home
  • Symbol of Hope, Peace, Prosperity, Tranquility, and Purification
  • A Lovely Gift for Sympathy, Get Well, Apology, etc
  • Low Maintenance House Plant

Description

The botanical name of the Peace Lily, Spathiphyllum, means peace and prosperity. What a perfect name for this beautiful plant. The Peace Lily is a symbol of hope, tranquility, rebirth, and purification so it is a perfect sympathy, get well, or apology gift. Many even believe the white bract flowers known as spathes resemble the white flag of surrender or the sails of a ship.

The Peace Lily is on NASA's list of top 10 air-cleaning plants. This amazing natural air purifier doesn't just add oxygen to your home; it filters and removes toxins including benzene, formaldehyde, ethylene, and ammonia!

These chemical compounds are thought to be responsible for Sick Building Syndrome so order a Peace Lily or 3 for your workplace and your home! The Peace Lily even absorbs mold spores. This houseplant is perfect for your bathroom. It can help eliminate yucky mildew! Plus this plant loves humidity so you'll have a happy plant too.

Glossy, green leaves float in the air on top of shimmery thick stems. The white, flower-like spathes pop up above the lush foliage. This plant has an elegant and tropical look.

The Peace Lily is a low-maintenance houseplant. Simply water it when the soil begins to dry. This plant can grow in low light conditions but needs medium to bright filtered light in order to produce its flower-like spathes. Fertilize with our balanced slow-release fertilizer twice per year for best growth.

This plant can be grown outside as a patio plant but needs some protection from the sun. It will need to be treated as an annual or moved in before temperatures drop below about 50 degrees.

Whether it is a gift, a way to purify your air, or you just want a bit of tranquil life in your home, the Peace Lily is one of the best choices in houseplants. Order yours today!

How To Care

Look for an area with filtered high to low light. Peace Lilies grow well in low light conditions but will not flower.

Allow the soil to dry between waterings. Test the soil by pushing your finger about 1 inch below the surface. Water when it is dry to the touch. You are much more likely to overwater a Peace Lily that underwater.

Spathiphyllum likes a humid environment. So be sure to keep your houseplant away from drafts. If your home is very dry your plant may benefit from misting the leaves a couple of times per week. Even if you completely neglect your Peace Lily and the leaves are drooping, don't panic! Give it a good watering including the leaves. It will likely revive within a day.

Keep your houseplant fertilized with our slow-release fertilizer about twice per year. The fertilizer will feed the plant slowly over time and encourage growth.

Repot when you notice that the soil seems to be drying out every couple of days or you see roots coming to the surface. These are indicators that your plant is becoming rootbound. Peace lilies like to be somewhat rootbound though so don't give your plant too much room in a new pot.

If you want to prune back fading flower bracts, prune close to the base of the plant. Another flower will not be produced on that stem.

This plant can be grown outside as a patio plant but needs some protection from the sun. It will need to be treated as an annual or moved in before temperatures drop below about 50 degrees.

For additional options, check out our beautiful Calla Lillies.

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SKU: 52342344640

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4.7 ★★★★★
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Hashi Hanta
Pawtucket, 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
Carnegie, 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.
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
Houston, 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
Chelsea, 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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