SKU: 88958738782
homalomena philodendron

homalomena philodendron Homalomena 'Maggy'

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Description

homalomena philodendron Homalomena 'Maggy'Homalomena rubescens Maggy Homalomena rubescens Maggy is a broad leaved tropical aroid with large, glossy green blades and warm reddish petioles. It grows as a full, clumping plant, with leaves rising from a compact base and spreading outward on thick, coloured stems. The heart shaped to oval leaves give the plant a lush, grounded shape, while the red tones in the petioles add colour between flushes of new growth. Homalomena rubescens Maggy grows best

Homalomena rubescens ‘Maggy’

Homalomena rubescens ‘Maggy’ is a broad-leaved tropical aroid with large, glossy green blades and warm reddish petioles. It grows as a full, clumping plant, with leaves rising from a compact base and spreading outward on thick, coloured stems. The heart-shaped to oval leaves give the plant a lush, grounded shape, while the red tones in the petioles add colour between flushes of new growth.

Homalomena rubescens ‘Maggy’ grows best in a warm indoor position with gentle light and an evenly moist root zone. Its wet-tropical Araceae background, from the eastern Himalayan region towards Myanmar, matches a chunky moisture-retentive substrate, stable warmth and protection from harsh direct sun.

Glossy heart-shaped leaves and red petioles

  • Growth habit: Forms a dense clump from a basal framework, with new leaves emerging from the centre.
  • Leaf shape: Broad, heart-shaped to oval green blades with a smooth surface and substantial feel.
  • Petiole colour: Reddish leaf stalks create a warm contrast below the green blades.
  • Family: Belongs to Araceae, the aroid family, with typical aroid flower structures when mature.
  • Indoor behaviour: Keep warm, evenly hydrated and protected from harsh direct sun.

How Homalomena rubescens ‘Maggy’ builds its clump

Homalomena rubescens is an accepted species in Araceae. Its native range is recorded from Sikkim to Myanmar, and it grows primarily in wet tropical conditions. In cultivation, ‘Maggy’ has a rounded leaf outline, glossy green surface and red-toned petioles. Like many aroids, it may produce a spathe and spadix, though the broad leaves and coloured petioles remain more visible indoors.

Homalomena rubescens ‘Maggy’ carries large leaves on fleshy petioles, so it can show moisture stress quickly, yet its roots still need air around them. A chunky, humus-rich substrate, drainage holes and careful watering keep the root zone lightly moist without becoming dense or sour.

Light, moisture and warmth for Homalomena rubescens ‘Maggy’

  • Light: Place in bright, indirect light or gentle filtered light. Strong midday sun can mark the broad leaves, while very low light slows new leaf production.
  • Watering: Water when the upper 20–30% of the substrate has started to dry. The root zone should stay lightly moist, never swampy or cold.
  • Substrate: Use an airy aroid mix with fine bark, coco fibre or peat-free houseplant base, perlite or pumice and a little composted material for moisture retention.
  • Humidity: Moderate to higher humidity helps new leaves expand more smoothly. A humidifier, grouping with other plants or a bright plant cabinet can help in dry indoor air.
  • Temperature: Keep warm, ideally around 18–28 °C. Avoid cold windowsills, cold wet substrate and sudden temperature drops.
  • Feeding: Feed lightly during active growth with a balanced houseplant fertiliser. Flush the substrate occasionally if mineral salts build up.
  • Repotting: Repot when roots fill the pot or watering becomes difficult to manage. Move up gradually rather than placing the plant into an oversized container.

Leaf symptoms that point to root stress

  • Yellowing lower leaves: Often linked to overwatering, old leaf turnover or a cool root zone. Check drainage, pot weight and substrate smell before watering again.
  • Soft petioles or collapsing leaves: Usually point to root stress from saturated substrate. Let the mix breathe, remove standing water and inspect the roots if the problem spreads.
  • Brown leaf edges: Can appear with dry air, irregular watering or salt build-up. Check moisture consistency and rinse the substrate through with clean water if needed.
  • Curling leaves: Often signal that the root zone dried too far or humidity dropped suddenly. Rehydrate gradually and keep the plant out of direct sun while it recovers.
  • Pests: Check the undersides of leaves, petiole bases and new growth for spider mites, thrips and mealybugs, especially during warm, dry periods.

Grooming the clump

Homalomena rubescens ‘Maggy’ keeps its broad basal shape when older leaves are removed selectively. Cut only fully yellowed or damaged leaves close to the base with clean scissors. Wiping dust from the glossy leaves helps light reach the leaf surface and makes pest checks easier.

Toxicity and handling

Homalomena rubescens ‘Maggy’ should be kept away from pets and children that chew plants. As an aroid, its tissues may irritate the mouth, throat or skin if damaged or ingested. Wear gloves if you have sensitive skin and wash your hands after pruning or repotting.

Homalomena rubescens name background

Homalomena rubescens belongs to Araceae. The genus name Homalomena is historically connected with a Malay plant name later interpreted in botanical literature through Greek-derived elements. The species epithet rubescens means becoming reddish, matching the red tones often seen in the petioles and floral parts. ‘Maggy’ is the horticultural cultivar name for this selection.

Homalomena rubescens ‘Maggy’ grows into a full, self-supporting aroid with broad glossy leaves and warm red petioles.

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Alexandria, US
★★★★★ 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
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Zygerian99
Louisville, US
★★★★★ 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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Shannon
Omaha, US
★★★★★ 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
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William P Ross
New York, 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.
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Reviewed in the United States on March 15, 2017
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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.
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Reviewed in the United States on May 22, 2026

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