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Description
aglaonema in pon Aglaonema 'Orange Flame'Aglaonema 'Orange Flame' Aglaonema 'Orange Flame' is a compact Chinese evergreen with a warm coloured crown and broad, glossy leaves held on fleshy petioles. It grows from short basal stems, gradually forming a dense indoor clump with new leaves rising from the centre. The foliage combines green and silver green patterning with orange pink midribs and warm petiole colour. That colour runs through the whole crown, especially where the leaf bases and
Aglaonema 'Orange Flame'
Aglaonema 'Orange Flame' is a compact Chinese evergreen with a warm-coloured crown and broad, glossy leaves held on fleshy petioles. It grows from short basal stems, gradually forming a dense indoor clump with new leaves rising from the centre.
The foliage combines green and silver-green patterning with orange-pink midribs and warm petiole colour. That colour runs through the whole crown, especially where the leaf bases and central veins catch the light.
Aglaonema 'Orange Flame' warm-tone traits
- Short-stemmed, clumping Aglaonema with a compact indoor shape
- Glossy oval leaves with green and silver-green markings
- Orange-pink midrib colour continuing into the petioles
- Full basal growth that suits shelves, sideboards and plant stands
- Steady foliage production in warm, filtered indoor light
Petiole colour, glossy leaves and growth
Aglaonema belongs to the Araceae family and grows as a rhizomatous evergreen perennial. In pots, Aglaonema 'Orange Flame' stays low and leafy, with each new petiole adding volume around the central crown.
The genus is native from north-eastern India through tropical Asia to Papua New Guinea. In cultivation, Aglaonema 'Orange Flame' needs warm rooms, filtered light and a lightly moist, aerated root zone.
Mature plants may produce small aroid flowers with a spathe and spadix. The inflorescence is modest; the coloured leaves and petioles carry the visible detail.
Care essentials for Aglaonema 'Orange Flame'
- Light: Place in bright to medium indirect light. A sheer curtain or a position beside an east or west-facing window protects the warm central colouring from scorch.
- Water: Water when the top 2–4 cm of substrate feels dry. The crown stays firmer when moisture is even and the pot drains fully after each watering.
- Substrate: Use a fine but airy mix with coco fibre or peat-free houseplant compost, perlite or pumice, and small bark pieces.
- Pot choice: Keep the plant in a pot with drainage holes and only a little extra root space. A compact crown in a large wet pot can lose roots quickly.
- Temperature: Keep between 18–28 °C. Orange-toned Aglaonema leaves mark easily after cold window contact or cold watering.
- Humidity: Average room humidity is acceptable. In heated rooms, steadier moderate humidity helps new leaves open with clean edges.
- Feeding: Feed lightly every 4–6 weeks from spring to late summer. A weak balanced fertiliser is enough for this slow to moderate grower.
- Repotting: Repot in spring when roots fill the pot or watering becomes difficult to balance. Increase pot diameter modestly.
- Grooming: Wipe the glossy leaves with a damp cloth and remove ageing lower leaves at the base with clean scissors.
- Propagation: Divide mature clumps only when several rooted shoots are present, then keep divisions warm while they re-establish.
Light, root and petiole checks
- Dry tan patches near the midrib: Check for direct sun, hot glass or sudden exposure after transport. Move the plant to softer filtered light.
- Yellowing lower leaves: A single old leaf can age naturally; several yellow leaves point to wet substrate or reduced root function.
- Soft petiole bases: Slide the plant from the pot and inspect the root ball. Trim damaged roots and move into a fresher, airier mix.
- Small new leaves: Check light level and root space. A very shaded position or a tight root ball can reduce leaf size.
- Brown tips: Review fertiliser strength, watering gaps and dry heat from radiators.
Safety note for Aglaonema 'Orange Flame'
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4.1 ★★★★★
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Product Reviews
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people.
THANK YOU Kent for writing this book.
We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022