SKU: 11930489432
variegated pothos price

variegated pothos price Snow Queen Pothos Starter Plant

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Description

variegated pothos price Snow Queen Pothos Starter PlantThe Snow Queen Pothos, also known as Epipremnum aureum 'Snow Queen' or simply the Variegated Pothos, is a popular and attractive houseplant known for its striking variegated foliage. The Snow Queen Pothos is characterized by its heart shaped leaves with creamy white to silvery gray variegation. The variegation varies in intensity, creating a beautiful contrast against the dark green background of the leaves. What do you get: A similar plant that is in

The Snow Queen Pothos, also known as Epipremnum aureum 'Snow Queen' or simply the Variegated Pothos, is a popular and attractive houseplant known for its striking variegated foliage. The Snow Queen Pothos is characterized by its heart-shaped leaves with creamy-white to silvery-gray variegation. The variegation varies in intensity, creating a beautiful contrast against the dark green background of the leaves.

What do you get:

A similar plant that is in the images in a 1.5" pot.

CARE:

Light: Provide bright, indirect light for your Snow Queen Pothos. It can tolerate lower light conditions but will exhibit the best variegation and growth in brighter, indirect light. Avoid direct sunlight, as it can scorch the leaves.

Temperature: Maintain a temperature range between 65°F to 80°F (18°C to 27°C). Avoid exposing the plant to cold drafts or sudden temperature fluctuations.

Humidity: While Snow Queen Pothos is adaptable to a range of humidity levels, it benefits from higher humidity. You can increase humidity by misting the plant, using a humidity tray, or employing a room humidifier.

Watering: Allow the top inch or two of the soil to dry out between waterings. Water thoroughly when you do water, and ensure proper drainage in the pot to prevent root rot. It's better to underwater than overwater this plant.

Soil: Use a well-draining potting mix, such as a standard indoor plant mix. Make sure the pot has drainage holes to prevent waterlogging.

NOTE: We prioritize the well-being of your plants throughout the delivery process, ensuring meticulous care from packaging to shipment. We promptly notify you once your package is dispatched and vigilantly monitor weather conditions. In the event of any delays necessitated by weather, we keep you informed. If your plant arrives with any damage, rest assured that we're here for you; simply reach out to us, and our dedicated team will provide assistance within 24 hours.

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SKU: 11930489432

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4.5 ★★★★★
Based on 6 reviews
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William P Ross
Phoenix, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Omaha, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Massapequa, US
★★★★★ 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
M
Verified Purchase
mackster
Fort Morgan, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Draper, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 25, 2018

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