SKU: 44498344610
aglaonema pictum tricolor why so expensive

aglaonema pictum tricolor why so expensive Chinese Evergreen 'Pictum Tricolor'

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Description

aglaonema pictum tricolor why so expensive Chinese Evergreen 'Pictum Tricolor'The Chinese Evergreen 'Pictum Tricolor' is a breathtaking variety of Aglaonema, prized for its striking camouflage like foliage. Its leaves feature an intricate blend of dark green, light green, and silvery white patches, creating a stunning tricolor effect. This rare plant brings an exotic and sophisticated touch to any indoor space. Native to Southeast Asia, this Aglaonema thrives in warm, humid environments. Known for its resilience, it adapts well

The Chinese Evergreen 'Pictum Tricolor' is a breathtaking variety of Aglaonema, prized for its striking camouflage-like foliage. Its leaves feature an intricate blend of dark green, light green, and silvery-white patches, creating a stunning tricolor effect. This rare plant brings an exotic and sophisticated touch to any indoor space.

Native to Southeast Asia, this Aglaonema thrives in warm, humid environments. Known for its resilience, it adapts well to indoor conditions with proper care, making it a favorite among collectors and plant enthusiasts seeking something extraordinary.

  • Common Names: Tricolor Chinese Evergreen, Camouflage Plant
  • Botanical Name: Aglaonema pictum 'Tricolor'
  • Size: Grows up to 12-24 inches tall indoors with proper care.

Care Instructions

  • Light: Prefers bright, indirect light to maintain its vibrant variegation. Tolerates medium light but may grow slower. Avoid direct sunlight, which can scorch its leaves.
  • Water: Allow the top inch of soil to dry out between waterings. Water thoroughly, ensuring proper drainage to prevent root rot.
  • Humidity: Thrives in moderate to high humidity. Mist regularly or place near a humidifier in drier environments.
  • Feeding: Fertilize every 4-6 weeks during the growing season (spring and summer) with a diluted liquid fertilizer. Reduce feeding during fall and winter.
  • Temperature: Maintain temperatures between 65°F and 80°F. Protect from cold drafts and temperatures below 60°F.
  • Soil: Use a well-draining potting mix rich in organic matter, such as one formulated for tropical plants.

Frequently Asked Questions

  1. Why are the leaves of my 'Pictum Tricolor' losing their variegation?
    Loss of variegation can result from insufficient light. Move the plant to a brighter spot with indirect sunlight to restore its vibrant colors.

  2. Does the 'Pictum Tricolor' require high humidity?
    While it thrives in moderate to high humidity, it can adapt to average indoor conditions with occasional misting.

  3. How do I propagate Chinese Evergreen 'Pictum Tricolor'?
    Propagation is typically done through stem cuttings or division. Ensure each cutting or division has healthy roots and foliage before planting.

Why Choose Chinese Evergreen 'Pictum Tricolor'?

The Chinese Evergreen 'Pictum Tricolor' is a rare and captivating plant that brings a touch of exotic beauty to any indoor space. Its unique, camo-like foliage, manageable care requirements, and compact size make it a standout choice for collectors and plant lovers alike.

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SKU: 44498344610

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Amazon Customer
Natrona Heights, 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
Dallas, 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
Los Angeles, 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
Battle Creek, 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
Lake Worth, 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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