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cat philodendron Philodendron 'Cream Splash' – Variegated Heartleaf PhilodendronPhilodendron hederaceum var. oxycardium 'Cream Splash' Care Guide and Insights Philodendron hederaceum var. oxycardium 'Cream Splash' is a striking variegated cultivar with heart shaped leaves featuring a unique blend of deep green, soft cream, and pale yellow hues. Each leaf has its own pattern, making every plant one of a kind. With cascading vines perfect for shelves or hanging planters, this Philodendron also climbs when given support. Its low
Philodendron hederaceum var. oxycardium 'Cream Splash' – Care Guide and Insights
Philodendron hederaceum var. oxycardium 'Cream Splash' is a striking variegated cultivar with heart-shaped leaves featuring a unique blend of deep green, soft cream, and pale yellow hues. Each leaf has its own pattern, making every plant one of a kind.
With cascading vines perfect for shelves or hanging planters, this Philodendron also climbs when given support. Its low-maintenance nature makes it a fantastic choice for adding vibrant foliage to your space without demanding care.
Key Features and Benefits
- Unique variegation: A mix of green, cream, and yellow on each leaf.
- Versatile growth habit: Suitable for trailing or climbing setups.
- Easy care: Thrives indoors with minimal maintenance.
Important Facts About Philodendron 'Cream Splash'
- Native Habitat: Grows in the tropical rainforests of Central and South America.
- Growth Rate: Moderate to fast under optimal conditions.
- Size: Vines can reach up to 1.2 meters indoors, with leaves growing 5-15 cm long.
- Toxicity: Contains calcium oxalate crystals and is toxic if ingested.
How to Care for Philodendron 'Cream Splash'
Light
- Prefers bright, indirect light but tolerates moderate to low light.
- Too much direct sun may scorch leaves; low light can reduce variegation.
- Grow lights can help maintain vibrancy in dim spaces.
Watering
- Allow the top 2-3 cm of soil to dry between waterings.
- Water thoroughly, ensuring excess drains away.
- Reduce watering in cooler months.
Humidity
- Prefers humidity above 50% but adapts to average indoor levels.
- A humidifier can help in drier environments.
Temperature
- Best range: 18-26°C.
- Avoid temperatures below 15°C and sudden drafts.
Soil
- Needs well-draining, nutrient-rich soil.
- A mix of perlite, and orchid bark supports root health.
Repotting
- Repot every 1-2 years or when roots outgrow the container.
- Choose a pot with drainage holes to prevent water buildup.
Fertilization
- Feed monthly with a diluted balanced liquid fertilizer.
- Avoid over-fertilizing to prevent salt buildup.
Propagation
- Propagate via stem cuttings in water or soil.
- Ensure cuttings have at least two nodes for better success.
Pruning
- Regular trimming encourages bushier growth.
- Remove damaged, yellowing, or leggy stems as needed.
Semi-Hydroponics
- Adapts well to semi-hydroponic growing with inert substrates.
- Use a balanced nutrient solution for hydroponic setups.
Common Issues and Solutions
Pests
- Spider mites: Fine webbing on leaves – treat with neem oil, or use beneficial insects or insecticidal soap.
- Mealybugs: White, cotton-like clusters – remove manually and treat with rubbing alcohol.
- Fungus gnats: Indicate overwatering – allow soil to dry and use sticky traps.
Root Rot
- Caused by excess moisture and poor drainage.
- Use a well-draining mix and avoid overwatering.
Leaf Discoloration
- Yellowing: Overwatering or nutrient deficiency.
- Browning tips: Low humidity or over-fertilization.
- Fading variegation: Needs more indirect light.
Fungal Issues
- Leaf spots: Black or brown fungal patches.
- Improve airflow and avoid overhead watering.
Additional Tips
- Encourage climbing for larger leaves by using a moss pole.
- Wipe leaves with a damp cloth to improve photosynthesis.
- Variegation is genetically stable and won’t revert.
Etymology
- Philodendron: Greek for "love tree," referencing its climbing habit.
- Hederaceum: Latin for "ivy-like," describing its growth pattern.
- Oxycardium: Greek for "sharp heart," referring to leaf shape.
- Cream Splash: Named for its creamy variegation.
Philodendron scandens vs. hederaceum – Clearing Up the Confusion
Botanical naming can be tricky. Philodendron scandens, Philodendron hederaceum, and Philodendron micans all refer to the same species: Philodendron hederaceum. The difference? Micans is a variety with velvety leaves.
Read more: Scandens, Hederaceum, Micans – A Comprehensive Heartleaf Philodendron Guide
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4.7 ★★★★★
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Product Reviews
★★★★★ 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
★★★★★ 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
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