SKU: 39150810069
is chamaedorea elegans an indoor plant

is chamaedorea elegans an indoor plant Parlor Palm Indoor Plant - Easy Care Low Light Houseplant

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Description

is chamaedorea elegans an indoor plant Parlor Palm Indoor Plant - Easy Care Low Light HouseplantABOUT THE PLANT The parlor palm is a small, attractive houseplant that is native to southern Mexico and Guatemala. It has thin bamboo like stems and fan like fronds that can grow up to 4 feet tall. This low maintenance plant is ideal for indoor spaces due to its ability to tolerate low light conditions and its air purifying qualities. The parlor palm is also a great stress reliever and can be easily propagated, making it an excellent addition to any

ABOUT THE PLANT
The parlor palm is a small, attractive houseplant that is native to southern Mexico and Guatemala. It has thin bamboo-like stems and fan-like fronds that can grow up to 4 feet tall. This low-maintenance plant is ideal for indoor spaces due to its ability to tolerate low light conditions and its air-purifying qualities.

The parlor palm is also a great stress-reliever and can be easily propagated, making it an excellent addition to any indoor environment.

WHAT YOU RECEIVE : Similar plant to the pictures in a 2" and 3” nursery pot.

CARE TIPS
Light: The parlor palm prefers bright, indirect light, but can also tolerate low light conditions. Avoid placing the plant in direct sunlight as this can burn the leaves.
Water: Water the plant when the top inch of soil feels dry to the touch. Avoid over watering as this can lead to root rot. Make sure the pot has good drainage to prevent water logging.

Humidity: The parlor palm prefers high humidity, so it is a good idea to mist the leaves with water regularly, especially during the winter months when indoor heating can cause the air to become dry.

Temperature: The parlor palm prefers temperatures between 60-85°F (16-29°C). Avoid placing the plant near drafty windows or doors.

Fertilizer: Feed the plant with a balanced, water-soluble fertilizer every 2-3 months during the growing season (spring and summer).

Pruning: Remove any yellow or brown leaves as they appear to maintain the plant's overall health and appearance.

Re-potting: Re-pot the plant every 2-3 years or when the roots have become too large for the current pot.

Propagated
Parlor palms can be propagated by division or by planting their seeds. Division involves separating the plant into smaller clumps, each with its own roots and leaves, and re-potting them in fresh soil. Planting seeds requires collecting the ripe seeds and planting them in a moist, well-draining soil mix, covering them lightly with soil and keeping them in a warm, humid environment until they germinate.

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SKU: 39150810069

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Amazon Customer
Massapequa, 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
Natrona Heights, 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
Massapequa, 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
Louisville, 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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