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Description
anthurium croatiana Anthurium crystallinum x luxuriansAnthurium crystallinum luxurians Anthurium crystallinum Anthurium luxurians is a cultivated Anthurium hybrid with broad heart shaped leaves, pale venation and thicker, more textured leaves as it matures. The Anthurium crystallinum parent brings velvet leaves and pale veins, while Anthurium luxurians adds darker, thicker leaves with a more puckered surface. Anthurium crystallinum is native from Panama to Colombia, while Anthurium luxurians is native to
Anthurium crystallinum × luxurians
Anthurium crystallinum × Anthurium luxurians is a cultivated Anthurium hybrid with broad heart-shaped leaves, pale venation and thicker, more textured leaves as it matures. The Anthurium crystallinum parent brings velvet leaves and pale veins, while Anthurium luxurians adds darker, thicker leaves with a more puckered surface.
Anthurium crystallinum is native from Panama to Colombia, while Anthurium luxurians is native to Colombia. This cultivated cross has no natural wild range. Individual plants can vary in leaf width, vein contrast, colour depth and surface texture.
Anthurium crystallinum × luxurians key traits
- Leaf shape: Broad heart-shaped leaves, often wider as the plant matures.
- Venation: Pale veins inherited from the Anthurium crystallinum parent.
- Texture: The surface may become more puckered, with a raised, quilted texture on mature leaves.
- Leaf substance: Usually heavier and firmer than plain Anthurium crystallinum.
- Variation: Individual plants can differ in darkness, vein contrast, leaf width and texture strength.
Anthurium crystallinum × luxurians indoor care
- Light: Provide bright filtered light. Harsh sun can mark the leaves, while too little light can slow growth and produce smaller leaves.
- Watering: Keep the mix lightly moist, with small dry periods between thorough waterings. Steady moisture helps new leaves expand without stress.
- Substrate: A chunky Anthurium mix with bark, coco chips, pumice or perlite holds light moisture while keeping air around the roots.
- Temperature: Warm, stable conditions support steady roots and mature leaf texture.
- Humidity and airflow: Moderate to high humidity supports clean new leaves. Airflow helps prevent damp conditions around textured foliage.
- Feeding: Keep feeding light; root quality and substrate structure matter more than feed strength.
- Repotting: Repot when roots fill the pot or the mix breaks down. Choose a pot close to the root ball so the mix does not stay wet for too long.
- Maturity: Leaves often become wider, more veined and more textured once the plant is mature and well rooted.
Anthurium crystallinum × luxurians issues to check
- Weak texture or small leaves: Often linked to immaturity, low light, weak roots or inconsistent moisture.
- Brown edges: Dry spells, strong fertiliser, low humidity or stressed roots can all cause brown edges.
- Yellowing leaves: This often points to a pot or mix that stays wet too long, especially in cooler conditions.
- Distorted new leaves: Inspect for thrips or mites and check whether humidity changed suddenly while the leaf was expanding.
- Soft base or root decline: Check roots quickly if the pot stays wet too long or the mix has collapsed.
- Uneven leaf traits: Natural variation between cultivated hybrid plants is normal.
Anthurium crystallinum × luxurians flowering
Mature plants can flower with a typical Anthurium spathe and spadix. Bloom colour and shape may differ between individual hybrids.
Anthurium crystallinum × luxurians handling and safety
Anthurium crystallinum × Anthurium luxurians should be kept out of reach of pets and children. Ingested plant material can irritate the mouth and throat. Sap may also irritate skin and eyes because Anthuriums contain insoluble calcium oxalate crystals.
Anthurium crystallinum × luxurians name background
Anthurium belongs to the family Araceae. The genus name comes from Greek words meaning flower and tail, referring to the spadix. The parent epithet crystallinum means crystal-like, referring to the pale venation, while luxurians means luxuriant or abundant.
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4.5 ★★★★★
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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
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