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Description
golden pothos latin name Epipremnum aureumEpipremnum aureum Epipremnum aureum is a tropical climbing aroid with flexible vines, glossy heart shaped leaves, and aerial roots that anchor to bark, moss poles, trellises, or other textured supports. In indoor pots it usually keeps its juvenile foliage, with green leaves marked by yellow to cream streaking, while supported mature plants can eventually produce larger, thicker leaves with a more divided outline. This species is often called golden
Epipremnum aureum
Epipremnum aureum is a tropical climbing aroid with flexible vines, glossy heart-shaped leaves, and aerial roots that anchor to bark, moss poles, trellises, or other textured supports. In indoor pots it usually keeps its juvenile foliage, with green leaves marked by yellow to cream streaking, while supported mature plants can eventually produce larger, thicker leaves with a more divided outline.
This species is often called golden pothos, devil’s ivy, or simply pothos in everyday plant trade, although Pothos is also a separate botanical genus. The plant sold as Epipremnum aureum belongs in Araceae and grows naturally as a wet-tropical climber from Mo‘orea in the Society Islands, where its stems use aerial roots to move upward through humid forest structure.
Golden pothos traits at a glance
- Evergreen aroid vine with trailing or climbing stems.
- Glossy juvenile leaves with a broad heart-shaped base.
- Green foliage with yellow to cream marbling and streaks.
- Aerial roots that attach readily to moss poles, bark boards, or rough supports.
- Node-based stems that can trail, climb, branch, or root from cuttings in indoor pots.
How this species climbs and fills a pot
Epipremnum aureum grows from nodes spaced along flexible stems. Each node can produce a leaf, an aerial root, and a new shoot, which makes the plant easy to prune, root, and train. In a hanging pot the stems cascade and create a loose curtain of foliage; on a vertical support the same plant directs growth upward and can develop larger leaves over time.
As a wet-tropical climber, Epipremnum aureum needs air as well as moisture around the roots. A loose substrate and a pot with drainage are essential. Warmth keeps growth active, while consistent bright indirect light helps leaves expand evenly and protects the glossy surface from scorch.
Care for strong vines and airy roots
- Light: Place in bright indirect light or soft filtered light. The plant tolerates medium light, but very dim placement slows internode growth and can make vines thinner.
- Water: Water when the upper 20–30% of the potting mix has dried. The stems recover well from slight drying, while saturated mix can weaken the fine roots.
- Substrate: Use an airy aroid mix with bark, perlite, coco chips, or similar coarse material so water drains quickly and oxygen reaches the root zone.
- Temperature: Keep between 18–28 °C for regular growth. Protect from cold windowsills, winter draughts, and temperatures below about 12–15 °C.
- Humidity: Average indoor humidity is usually tolerated. Higher humidity helps new leaves expand more smoothly, especially on climbing stems.
- Feeding: Feed lightly during active growth with a balanced fertiliser. Reduce feeding in winter or under low light.
- Support and pruning: Let vines trail, or guide them onto a moss pole for stronger upward growth. Prune above a node to encourage branching and root cuttings from healthy stem pieces.
Problems that show up on older vines
- Yellow lower leaves: Check whether the potting mix has stayed wet for too long. Let the mix dry further and improve drainage before watering again.
- Brown, dry leaf edges: Look for irregular watering, strong sun, salt build-up, or dry heat near radiators. Flush the mix occasionally and move the plant away from hot air.
- Long bare sections: Increase light gradually and prune leggy stems back to active nodes so new shoots can fill in closer to the pot.
- Soft stems near the base: Inspect the roots and lower nodes. Soft, dark tissue usually points to overwatering, cold wet substrate, or poor aeration.
- Sticky leaves or speckling: Check the undersides and stem joints for scale, mealybugs, thrips, or mites, then isolate and treat early.
Safety around pets and children
Epipremnum aureum contains insoluble calcium oxalate crystals. Chewed leaves or stems can irritate the mouth, lips, tongue, and digestive tract, so keep the plant away from pets and small children. Wear gloves if your skin reacts easily to aroid sap.
Botanical name background
The genus name Epipremnum comes from Greek roots meaning “upon” and “trunk,” a reference to its climbing habit. The species epithet aureum means “golden,” matching the yellow-gold variegation associated with the classic cultivated plant.
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4.5 ★★★★★
Based on 18 reviews
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Product Reviews
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience.
If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference.
I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance:
The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals).
Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning.
Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time.
And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
★★★★★ 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
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