SKU: 21360494303
violin philodendron

violin philodendron Philodendron Violin Variegated (Highly variegated)

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Description

violin philodendron Philodendron Violin Variegated (Highly variegated)The Philodendron pedatum Violin Variegated is a striking rarity with elegant leaf fingers. It is a relatively easy houseplant, easily adapting to a living room climate. Its aesthetic value is mainly in its variegated leaves. Origin The Philodendron pedatum is native to the tropical rain forests of South America. This particular variety nicknamed 'Violin' owes its name to the shape of the leaf, which is reminiscent of a violin. The variegated variety

The Philodendron pedatum Violin Variegated is a striking rarity with elegant leaf fingers. It is a relatively easy houseplant, easily adapting to a living room climate. Its aesthetic value is mainly in its variegated leaves.

Origin

The Philodendron pedatum is native to the tropical rain forests of South America. This particular variety nicknamed 'Violin' owes its name to the shape of the leaf, which is reminiscent of a violin. The variegated variety is rare in culture and is only propagated by a small number of specialized growers.

Characteristics

This variegated pedatum immediately stands out because of its deeply incised leaves, which become more pronounced in shape with age. The variegated markings vary per leaf from cream to yellow or sometimes almost white-green areas, beautifully scattered over the dark green leaf surface. Each leaf is unique. As the plant ages and receives more light, the incisions become deeper and the variegation more pronounced. The veins remain clearly visible, giving the plant an elegant character. A rare find for the true enthusiast.

Care

Light

Place the plant in a bright spot without direct sunlight. Too much sun can burn the variegated parts, while too little light reduces variegation.

Temperature

Ideally between 18 and 28°C. Protect from drafts and cold nights below 15°C.

Substrate

Use an aerated, well-draining mixture based on fine tree bark, coconut fiber and possibly sphagnum. This mimics the natural conditions in which this epiphyte grows.

Humidity

A humidity level above 60% is recommended. In drier air, a humidifier or regular spraying can help keep the foliage in top condition.

Nutrition

During the growing season, give a light dose of balanced, fertilizer for aroids once a month. Preferably use a fertilizer without excessive nitrogen to keep the leaf markings beautiful.

Watering

Allow the top layer of the substrate to dry slightly between waterings. Preferably use demineralized water or clean rainwater at room temperature. Overwatering should be avoided to prevent root rot. Do not leave water at the bottom of the pot for too long.

Flowering

Although flowering in room conditions is rare, under ideal conditions this species can bloom with inconspicuous bracts. However, the ornamental value is entirely in the striking foliage.

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SKU: 21360494303

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Birmingham, 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
San Leandro, 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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Richard Hackathorn
Belleville, 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
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Verified Purchase
Amazon Customer
Battle Creek, 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
K
Verified Purchase
Kindle Customer
Houston, 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

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