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Description
monstera adansonii aurea care Monstera adansonii AureaMonstera adansonii variegata 'Aurea' Monstera adansonii variegata 'Aurea' brings yellow toned variegation to the Swiss cheese vine. Its green leaves can show lime, chartreuse or golden yellow sections, with the same narrow adansonii blade shape and oval fenestrations. The warm yellow tones give the leaves a golden green look, especially as new growth hardens and the contrast becomes clearer. The plant grows from a flexible vine with nodes and aerial
Monstera adansonii variegata 'Aurea'
Monstera adansonii variegata 'Aurea' brings yellow-toned variegation to the Swiss cheese vine. Its green leaves can show lime, chartreuse or golden-yellow sections, with the same narrow adansonii blade shape and oval fenestrations. The warm yellow tones give the leaves a golden-green look, especially as new growth hardens and the contrast becomes clearer.
The plant grows from a flexible vine with nodes and aerial roots. A supported stem keeps nodes aligned and prevents long trailing sections from tangling. Because yellow variegated tissue contains less chlorophyll than green tissue, yellow-heavy vines often grow more slowly, especially when roots are recently disturbed.
Monstera adansonii 'Aurea' key features
- Colour: Yellow, lime or golden variegation over green fenestrated foliage.
- Leaves: Narrow adansonii blades with natural oval holes.
- Stem: Vining growth with nodes, aerial roots and flexible training options.
- Growth pace: Often slower than green Monstera adansonii when the variegation is heavy.
Yellow variegation and vine growth
Monstera adansonii comes from tropical American forest habitats, where it grows as a climbing aroid. 'Aurea' keeps that same vining structure indoors. The stem produces leaves one node at a time, and each new leaf can carry a different balance of green and yellow tissue.
Yellow sections are more delicate than fully green leaf areas. They can mark from intense sun, irregular watering or a stressed root zone. A stable position, open substrate and careful watering reduce dry brown marks on yellow sections while the vine develops.
Flowering indoors is uncommon. Mature Monstera adansonii can produce a spathe and spadix under suitable tropical conditions; indoor plants usually remain foliage vines with yellow-variegated leaves.
Care for yellow-variegated Monstera adansonii
- Light: Place in bright indirect light. Shield yellow sections from intense midday sun, especially behind hot glass.
- Watering: Let the upper half of the pot dry before watering again. Do not leave the root ball cold and saturated.
- Substrate: Choose an open aroid mix with bark, coco chunks and pumice or perlite for drainage and root oxygen.
- Temperature: Maintain 18–27 °C where possible. Cold wet roots can lead to yellowing and dark leaf marks.
- Humidity: Moderate to higher humidity keeps thin new leaves from drying at the edges as they expand.
- Training: Start the vine on a pole, plank or trellis before the stem becomes long and tangled.
- Feeding: Feed lightly enough to avoid salt buildup on yellow leaves; a small regular dose is safer than a strong monthly charge.
- Pruning: Cut above a node to control length or root cuttings. Keep cuttings with a growth point and enough green tissue.
- Repotting: Shift to fresh open mix when roots are dense at the pot edge, keeping the yellow-variegated vine in a modest container.
Common Monstera adansonii 'Aurea' problems
- Dry brown yellow areas: Check sun exposure, watering gaps, humidity and fertiliser strength.
- Whole leaves turning yellow: Inspect the roots and potting mix. Dense wet substrate can reduce oxygen around the root system.
- Little new growth: Check warmth, root health and green leaf area. Very yellow growth can move more slowly.
- Thin extended stems: Increase bright indirect light and give the vine a support to climb.
- Damaged new leaves: Look for pests, dry unfurling conditions or root stress before cutting fresh growth.
Pet and child safety
Monstera adansonii variegata 'Aurea' contains insoluble calcium oxalate crystals. Chewed leaves or stems can irritate the mouth, throat and digestive tract, so keep the plant and pruning waste away from pets and children.
Botanical name and Aurea meaning
Monstera adansonii Schott is an accepted species in Araceae. Monstera is linked with unusual leaf forms in the genus, and adansonii honours French botanist Michel Adanson. Aurea is Latin for golden, matching the yellow tone of the variegation.
Each node can carry a different yellow-green balance, so Monstera adansonii variegata 'Aurea' develops with natural variation along the stem.
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4.1 ★★★★★
Based on 29 reviews
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Product Reviews
★★★★★ 5
Very well built
Color: White
Yes it was really nice my granddaughter love's the jewelery box
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Reviewed in the United States on November 18, 2025
★★★★★ 4
Lots of storage space, great mirrors, has paint scent
Color: White
You can store a lot in this! There are a lot of good things to say about this. It's cute, has tons of mirror space and room to store jewelry, seems pretty sturdy as far as the wood parts go, and could also be used as Barbie furniture for a girl with a little imagination--a perfect armoire! It's the perfect height for a Barbie doll.
So, there is space for non-dangly earrings to be placed in holes, a section under that for necklaces or bigger earrings, four small spots on top for necklaces or other chains, a larger section there, five drawers, four long necklace spots to hang them with catch sections underneath, and a space for a lot of rings. There are also eight hangers for short chains or earrings.
Quality wise, it is decent, but maybe not fantastic. Most of the wood aspects seem strong and built well, except the part that stores rings comes out completely and can be a tiny bit of a challenge to put back in a way that it fits right. The cardboard inserts are a little flimsy. I think if a child used this and pulled down on one of the necklace holders, it might not spring back into place, and then would be useless after that. The cardboard insert on the top for the stud earrings also seems like it will get misshapen pretty easily.
The biggest negative, and is one I am not sure I can get beyond, but time will tell, is the strong paint odor of it. It smells like a bedroom smells immediately after it gets painted--that kind of smell that tells you perhaps you should sleep somewhere else for a night. That smell fades, though. I hope this smell also fades because it seems too strong to give to a child, and this is advertised as for a woman or girl (which I assume is a child.)
So, size wise and usage is generally good for this. If the paint smell fades, I will be fully satisfied with it.
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Reviewed in the United States on October 18, 2025
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it.
For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would.
The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is.
This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
★★★★★ 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
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