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Description
dracaena marginata care indoor Small size Dracaena Marginata - Air Purifying Houseplant for Your HomeDescription Light Soil Water Hardiness Dracaena Marginata, or Dragon Tree, is a member of the Dracaena family which includes about 120 plant species. Native to Madagascar, Dracaena Marginata has bare trunks and clusters of thin, long leaves. The leaves are dark green with red edges. Dracaena Marginata is a very popular houseplant because it is easy to care for and can adapt to different indoor lighting conditions. This plant can grow up in height up
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Dracaena Marginata, or Dragon Tree, is a member of the Dracaena family which includes about 120 plant species. Native to Madagascar, Dracaena Marginata has bare trunks and clusters of thin, long leaves. The leaves are dark green with red edges.
Dracaena Marginata is a very popular houseplant because it is easy to care for and can adapt to different indoor lighting conditions. This plant can grow up in height up to 20 feet outdoors and 8 feet indoors.
Dracaena Marginata is also on the list of NASA's air-purifying plants. It is very effective in cleaning the indoor air, making this plant an ideal houseplant for any living space.
Small size Dracaena Marginata is planted in a 4-inch grow pot.
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Dracaena Marginata prefer medium bright, indirect sunlight but it also tolerate low-light conditions. Deep shade, however, can slow down its growth. Avoid direct sunlight to prevent sunburn.
This plant is drought-tolerant and does not need frequent watering. Only water when the soil is dry to the touch. Check the leaves often. Yellow leaves means that the plant is under-watered while brown leaves indicates that you have been over-watering your Marginata. Only use distilled water or another non-fluoridated water.
The best soil for this plant is well-drained, loamy potting soil. You can mix potting soil with perlite and loam.
This plant thrives in temperatures of 60°F-80°F. It is vital to ensure the room temperature does not fall below 60°F. Marginata thrives in normal room humididy but it is recommended to mist this plant frequently.
This plant should be fertilized once during the growing season (spring/summer) with diluted houseplant fertilizer.
Marginata is toxic to cats and dogs, so keep this plant away from your pets. -
USDA Zone 10-12
USDA Zone 9b: to -3.8 °C (25 °F)
USDA Zone 10a: to -1.1 °C (30 °F)
USDA Zone 10b: to 1.7 °C (35 °F)
USDA Zone 11: above 4.5 °C (40 °F)
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4.7 ★★★★★
Based on 28 reviews
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Product Reviews
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning.
There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s.
The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read.
There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning.
The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique.
Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry.
The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff!
If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper.
As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture.
So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money.
The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence.
There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions.
The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work.
They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively.
There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning.
I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on August 25, 2018