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Description
marble queen pothos online Marble Queen PothosDescription Light Soil Water Hardiness Marble Queen Pothos, or Epipremnum aureum 'Marble Queen', is an exquisite vining plant with lovely white and cream variegation on its heart shaped green leaves. Native to French Polynesia and other tropical regions, its vines can span up to 20 feet long in the wild, and stay under 10 feet long as a houseplant. This plant is best known as a hard to kill houseplant, since it is easy to care for and thrives very
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Marble Queen Pothos, or Epipremnum aureum 'Marble Queen', is an exquisite vining plant with lovely white and cream variegation on its heart-shaped green leaves. Native to French Polynesia and other tropical regions, its vines can span up to 20 feet long in the wild, and stay under 10 feet long as a houseplant.
This plant is best known as a hard-to-kill houseplant, since it is easy to care for and thrives very well in a variety of living conditions. Marble Queen Pothos does not have much demand for light, making it a perfect low-maintenance houseplant and best for beginners.
The Marble Queen Pothos is a favored feng shui plant due to its lush, variegated leaves that symbolize prosperity and abundance. Its hardy nature and trailing vines are believed to attract positive energy and purify the air, enhancing the overall harmony and balance in any space.
Marble Queen Pothos can produce small white flowers only when the vines and leaves have reached their mature sizes, meaning it is rare to see them bloom indoors.
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Marble Queen Pothos can tolerate low light conditions but too little light will slow its growing ability. Place the plant in bright, indirect light to nurture its variegated leaves. Avoid exposing Marble Queen to harsh direct sunlight to prevent parching the leaves.
This plant is drought tolerant and only needs watering when the top few inches of the soil have dried out. It enjoys moist soil during summer days but not too soggy, so drains well after watering.
Marble Queen Pothos prefers rich, well-draining soil mixture. A standard indoor potting soil with peat moss, vermiculite, or perlite is ideal for this plant.
This plant thrives best at average room temperature (between 65°F - 85°F). Cold weather will stunt its growth and can permanently damage the leaves when the temperature is under 55°F. The best condition for Pothos is a warm and humid environment. Using humidifier and misting the plant occasionally to increase humidity is recommended.
This plant should be fertilized once during the growing season (spring/summer) with diluted houseplant fertilizer.
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USDA Zone 11a-12b
USDA Zone 9b: to -3.8 °C (25 °F)
USDA Zone 10a: to -1.1 °C (30 °F)
USDA Zone 10b: to 1.7 °C (35 °F)
USDA Zone 11: above 4.5 °C (40 °F)
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4.7 ★★★★★
Based on 10 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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