SKU: 84988380950
philodendron xanadu toxic

philodendron xanadu toxic Thaumatophyllum (Philodendron) 'Xanadu' XL

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Description

philodendron xanadu toxic Thaumatophyllum (Philodendron) 'Xanadu' XLA bold green statement for your home jungle Thaumatophyllum (Philodendron) Xanadu XL Fast EU shipping Grown with love in the EU Not pet safe Summary: With its deeply lobed, glossy leaves and bushy, fountain like growth, the Xanadu XL brings instant tropical flair to European living spacesperfect for filling corners and brightening modern German apartments. Why You'll Love the Thaumatophyllum Xanadu XL Compact yet lushideal for medium to large rooms.

A bold green statement for your home jungle

Thaumatophyllum (Philodendron) Xanadu XL | Fast EU shipping | Grown with love in the EU | Not pet-safe

Summary: With its deeply lobed, glossy leaves and bushy, fountain-like growth, the Xanadu XL brings instant tropical flair to European living spaces—perfect for filling corners and brightening modern German apartments.

✨ Why You'll Love the Thaumatophyllum Xanadu XL

  • Compact yet lush—ideal for medium to large rooms.
  • Architectural leaves that look stunning in designer pots.
  • Low-maintenance compared to many other philodendrons.
  • XL size (Pot Ø21 cm, Height ~50 cm) for an immediate green impact.

🌞 Light & Placement

This Xanadu thrives in bright, indirect light. It tolerates partial shade, making it a great choice for north- or east-facing German windows. Avoid harsh midday sun that can scorch the leaves.

💧 Water & Humidity

Water when the top 3–4 cm of soil feels dry. Ensure good drainage to prevent root rot. Appreciates higher humidity but adapts well to standard European indoor climates—occasional misting keeps leaves glossy.

🪴 Soil & Potting

Use a loose, airy soil mix rich in organic matter. A blend of peat-free potting soil with perlite or bark chips works perfectly. Repot every 2–3 years to refresh nutrients and support its bushy growth.

🐾 Toxicity & Safety

Like most Philodendron relatives, Xanadu contains calcium oxalates and is toxic to pets and children if ingested. Place out of reach of curious cats and dogs.

🌱 Growth & Propagation

The Xanadu XL is a clumping grower, producing dense foliage rather than climbing. Propagate by careful division during repotting. With the right care, it can spread up to 1 m wide indoors.

📆 Seasonal & Special Care

During spring and summer, fertilise monthly with a balanced liquid fertiliser. Growth slows in autumn and winter—reduce watering and skip fertilisation. Keep away from cold drafts in winter.

🐛 Common Issues

  • Yellowing leaves: often a sign of overwatering.
  • Crispy edges: too much direct sun or low humidity.
  • Pests: occasionally attracts spider mites—wipe leaves regularly to prevent.

🧬 Botanical Background

Once classified as Philodendron Xanadu, this plant now belongs to the genus Thaumatophyllum. Native to Brazil, it has become a beloved houseplant across Europe for its lush, tropical presence and manageable growth habit.

🛒 Ready to transform your home into a jungle paradise?

Add Thaumatophyllum Xanadu XL to your cart and enjoy fast, secure shipping across Germany and the EU!

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SKU: 84988380950

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4.1 ★★★★★
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Par
Bozeman, 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
Lexington, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Lake Worth, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Los Angeles, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Omaha, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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