SKU: 74296662327
philodendron cebu blue epipremnum pinnatum

philodendron cebu blue epipremnum pinnatum Epipremnum Pinnatum 'Cebu Blue'

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Description

philodendron cebu blue epipremnum pinnatum Epipremnum Pinnatum 'Cebu Blue'A robust climbing vine with almost blue leaves. There are many (cultivated) varieties of this plant. 'Cebu Blue' has silvery, greenish blue leaves that become much more intensely silvery metallic, especially on larger plants. 'Cebu Blue', as the name suggests, comes from Cebu, one of the larger Philippine islands. It is probably not a cultivated form, but a natural form. Botanical description. Originally from Southeast Asia, this species has now been

A robust climbing vine with almost blue leaves.

There are many (cultivated) varieties of this plant. 'Cebu Blue' has silvery, greenish-blue leaves that become much more intensely silvery-metallic, especially on larger plants. 'Cebu Blue', as the name suggests, comes from Cebu, one of the larger Philippine islands. It is probably not a cultivated form, but a natural form.

Botanical description.

Originally from Southeast Asia, this species has now been introduced almost everywhere in the tropics and subtropics. The plant has low requirements and is very robust, making it a popular and, above all, easy-care indoor plant.

Appearance.

Like other Epipremnums, E. pinnatum 'Cebu Blue' is a climber and only grows its large, fully developed and coloured leaves when it can climb. It can also be grown as a hanging plant, but then it remains much smaller.

As a young plant, Epipremnum pinnatum 'cebu blue' will trail along the ground until it finds something to climb, usually a tree. At this stage of its life, it is small, can handle very little light, and has rather inconspicuous leaves.

Once it has found a tree, it climbs it and attaches itself with its aerial roots. From now on, it needs more light and less moisture. Only then does it develop the leaves that are typical of the species, which also become much larger.

Care.

This cold-hardy plant likes a lot of light, but not direct sun at midday. It thrives best in a location with bright but indirect light, for example directly by a west/east window or slightly to the side of a south-facing window. A little direct morning sun, or a little more direct sun during the winter, will not harm it. However, you should be careful not to damage the leaves. Direct sunlight can "burn" the leaves, especially after longer periods of cloudy weather.

This species will only form its adult leaves if it gets both enough light and a support to climb on. This could, in nature, be a tree trunk, but it could also be another fairly rough surface, such as a wooden plank.

You can also use a so-called " moss pole ", which retains more moisture. Epipremnum pinnatum 'cebu blue' also wants to be planted in a well-drained soil mixture that is allowed to be moist without being wet, like our aroid soil. Buy Epipremnum pinnatum 'cebu blue' online!

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SKU: 74296662327

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Amazon Customer
Phoenix, 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
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Kindle Customer
Cuba, 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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Tommy Jonsson
Pawtucket, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
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Moses Kayanda
Carnegie, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
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Gabe Rigall
Battle Creek, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
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Reviewed in the United States on February 26, 2022

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