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Description
wax plant indoor Hoya australis Waxvine PlantHoya australis, commonly known as Waxvine, Porcelain Flower, and Honey Plant, is a classic, easygoing vining houseplant loved for its thick, glossy green leaves and clusters of sweetly fragrant, star shaped blooms. Native to Australia and parts of the South Pacific, this vigorous climber naturally grows along rainforest edges and rocky coastal regions, making it both adaptable and rewarding indoors. Train it up a trellis or let it trail from a shelf
Hoya australis, commonly known as Waxvine, Porcelain Flower, and Honey Plant, is a classic, easygoing vining houseplant loved for its thick, glossy green leaves and clusters of sweetly fragrant, star-shaped blooms. Native to Australia and parts of the South Pacific, this vigorous climber naturally grows along rainforest edges and rocky coastal regions, making it both adaptable and rewarding indoors.
Train it up a trellis or let it trail from a shelf or hanging basket—Hoya australis ‘Waxvine’ brings a lush, tropical look with minimal fuss, making it a great pick for beginners and seasoned Hoya collectors alike.
Why You’ll Love Hoya australis
- Fragrant Blooms: Produces clusters (umbels) of porcelain-like, star-shaped flowers with a sweet scent.
- Glossy, Waxy Foliage: Thick leaves stay attractive year-round and help the plant tolerate occasional missed waterings.
- Easy & Adaptable: Does well in average indoor humidity and a range of bright-to-medium light conditions.
- Trailing or Climbing Habit: Ideal for hanging baskets, shelves, trellises, or moss poles.
- Pollinator Friendly (Outdoors): In its native habitat, flowers can attract pollinators—including butterflies.
Plant Profile
- Botanical Name: Hoya australis
- Common Names: Waxvine, Porcelain Flower, Honey Plant
- Family: Apocynaceae
- Native Range: Australia; parts of the South Pacific
- Growth Habit: Trailing / climbing vine
- Toxicity: Commonly listed as non-toxic, but as with any plant, discourage chewing and keep out of reach of pets/kids who like to nibble.
Care Guide
- Light: Best in bright, indirect light. Can tolerate medium light, but brighter light improves growth and blooming. Avoid intense, direct midday sun.
- Water: Let the top 1–2 inches of soil dry between waterings. Water thoroughly, then allow excess to drain. Overwatering is the fastest way to stress a Hoya.
- Humidity: Average home humidity is typically fine. Higher humidity can support faster growth and easier blooming, but it adapts well.
- Temperature: Prefers 60–85°F (16–29°C). Protect from cold drafts and sudden temperature drops.
- Soil: Use a chunky, well-draining mix (e.g., potting mix amended with orchid bark and perlite/pumice) to keep roots airy.
- Fertilizer: Feed monthly during spring/summer with a diluted balanced fertilizer. Reduce or pause feeding in fall/winter.
- Support: Provide a trellis or hoop if you want upward growth; Hoyas often bloom more readily on mature vines.
Propagation
Propagation is simple with stem cuttings. Take a cutting with 2–3 nodes, remove the lowest leaves, and root in water, sphagnum, or a chunky Hoya mix. Keep warm with bright, indirect light for best results.
Note: Avoid removing old flower spurs (peduncles)—Hoyas often rebloom from the same spurs.
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4.7 ★★★★★
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Product Reviews
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid.
I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets.
As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before.
I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt.
PROS:
- Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way.
- Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops.
- Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages.
- Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them.
- PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly.
CONS:
- Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste.
- Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
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Reviewed in the United States on February 26, 2022