SKU: 28200997086
lily flower pot

lily flower pot Lily of the Valley - Patio Kit

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Description

lily flower pot Lily of the Valley - Patio KitPRESALE Spring 2026 Shipping Schedule: 3 20 2026 Lily of the Valley bears dainty, white flowers with a strong, sweet perfume in spring. Bell like blooms hang from semi pendulous, arching stems with lance like foliage. It can be an aggressive groundcover given the right light conditions and rich, moist soil. Lily of the Valley tolerates drought in shaded locations. They are non invasive and deer resistant. They can also be grown successfully in outdoor

PRESALE

Spring 2026 Shipping Schedule: 3/20/2026

Lily of the Valley bears dainty, white flowers with a strong, sweet perfume in spring. Bell-like blooms hang from semi-pendulous, arching stems with lance-like foliage. It can be an aggressive groundcover given the right light conditions and rich, moist soil. Lily of the Valley tolerates drought in shaded locations. They are non-invasive and deer resistant. They can also be grown successfully in outdoor containers or indoors as a house plant. These plants have been around since 1000 B.C. and are often found near old foundations, where they proved more durable than the houses.

About this Variety

Our Patio Lily Kit comes with everything you need to grow this exotic plant. One large packaged Lily of the Valley root, a 5x7" trendy green metal decorative outer pot, a 5" nursery pot, a block of compressed soil and growing directions.

Highlights

  • Deer & critter resistant
  • Elegant flower stems are perfect for cut flowers
  • Drought tolerant in shaded locations
  • Returns and blooms for years to come
  • Grows well in containers, garden beds and borders

Exposure:

Partial to Full Shade

Blooms:

Mid Spring

Height:

Grows 6-8" tall

Spacing/Depth:

Plant 4-6" apart, 2-3" deep

USDA Zones:

Grow as Perennial in zones 2-8. Grow as Annual elsewhere.

Growing Instructions

Patio Container Kit Planting: Plant early spring so the bulb can get established before the hot weather. They love the sun so consider that when choosing a location, however some afternoon shade will extend bloom time. Simply expand the soil, fill the nursery pot with 2/3 of the growing medium, position the bulb - roots down, pointy end up - and cover with the remaining soil. Water until moist and as needed, not letting the pot dry out.

Outdoor Planting: Plant Lily of the Valley outdoors in spring after all danger of frost has passed in a partially shaded location. The ideal soil is rich and porous and drains well, yet still holds enough water for the roots. If your soil is heavy clay and drains slowly, or very sandy and does not hold water, add organic soil amendments like peat moss or ground bark. Spread a 3-4 inch layer of soil amendment if needed and incorporate into the soil to a depth of 10 inches. Plant roots 2-3" deep and 4-6" apart. Cover with soil. Water thoroughly at planting. While actively growing, water frequently and thoroughly so that water will reach roots. Mulch to keep the soil cool and to prevent the soil from drying out. Fertilize during growing period.

Care Tip

Remove foliage after it has completely yellowed and died back.

Shipping Notes
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Exchange/Return Notes
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SKU: 28200997086

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Carol
Battle Creek, 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.
Chelsea, 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
Carnegie, 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
Phoenix, 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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Verified Purchase
Amazon Customer
Chelsea, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025

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