SKU: 78516436734
aglaonema prayer plant

aglaonema prayer plant Aglaonema 'Siam' Plant

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Description

aglaonema prayer plant Aglaonema 'Siam' PlantAglaonema Siam also known as Chinese Evergreen Siam is a vibrant tropical houseplant that brings bold color and effortless beauty to any indoor space. With its stunning red pink stems and deep green, lance shaped leaves edged in crimson, this low maintenance stunner is perfect for brightening up your home or workspace. Additionally, they are known for being air purifying plants, contributing to a healthier indoor environment. Available in a 6" nursery

Aglaonema ‘Siam’ – also known as Chinese Evergreen ‘Siam’ – is a vibrant tropical houseplant that brings bold color and effortless beauty to any indoor space. With its stunning red-pink stems and deep green, lance-shaped leaves edged in crimson, this low-maintenance stunner is perfect for brightening up your home or workspace. Additionally, they are known for being air-purifying plants, contributing to a healthier indoor environment.

Available in a 6" nursery planter or elevated in our stylish Selene 6" Marble Planter for a polished, modern finish.

🌿 Care Guide:

  • Light: Thrives in bright, indirect light, but can also adapt to lower light conditions. Avoid harsh direct sun, which can scorch its leaves.

  • Watering: Water when the top 1–2 inches of soil feel dry. Be sure to let excess water drain—this plant doesn't like soggy roots.

  • Humidity: Appreciates moderate to high humidity but tolerates normal household levels. Mist occasionally or place near a humidifier in dry environments.

  • Temperature: Keep in warm environments between 65–85°F. Avoid cold drafts.

  • Soil: Use a well-draining potting mix, ideally with perlite or sand for aeration.

  • Fertilizer: Feed monthly during the growing season (spring and summer) with a balanced liquid houseplant fertilizer.

🐾 Is it Pet Friendly?

Unfortunately, Aglaonema 'Siam' is not pet-safe. It contains calcium oxalate crystals, which can be toxic if ingested by cats or dogs. Keep out of reach of curious pets.

🌟 Fun Facts:

  • Native to Southeast Asia, Aglaonema has been grown indoors for centuries thanks to its tolerance of low light and humidity.

  • This plant is part of NASA's list of air-purifying houseplants, helping to remove toxins from indoor air.

  • In many cultures, Aglaonemas are considered lucky plants, thought to bring good fortune and positive energy.

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SKU: 78516436734

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Carol
Waukegan, 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.
New York, 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
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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Verified Purchase
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
A
Verified Purchase
Amazon Customer
Lowell, 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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