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Description
aglaonema order Aglaonema 'Spring Red'Aglaonema 'Spring Red' Aglaonema 'Spring Red' is a compact Chinese evergreen grown for warm red pink colour across a dense, upright clump. New leaves rise from short stems and build a rounded crown with visible colour across the upper leaves. The leaves are smooth, oval to lance shaped and slightly glossy, with red to salmon pink colouring near the midrib and green tissue around the blade edge. As new growth expands, the colour sits close to the
Aglaonema 'Spring Red'
Aglaonema 'Spring Red' is a compact Chinese evergreen grown for warm red-pink colour across a dense, upright clump. New leaves rise from short stems and build a rounded crown with visible colour across the upper leaves.
The leaves are smooth, oval to lance-shaped and slightly glossy, with red to salmon-pink colouring near the midrib and green tissue around the blade edge. As new growth expands, the colour sits close to the centre of the leaf, giving the crown a warm core with a cooler green outline.
Aglaonema 'Spring Red' colour profile
- Compact, clumping Chinese evergreen with short upright stems
- Red-pink central colour with green margins and soft irregular markings
- Smooth, broad leaves held in a dense rounded crown
- Slow to moderate indoor growth with new leaves forming from the centre
- Keep warm, evenly moist and away from direct midday sun
Aglaonema 'Spring Red' crown development
Aglaonema 'Spring Red' is a cultivated Aglaonema selection grown for patterned foliage, compact crown growth and warm red-pink colour. It carries the familiar Chinese evergreen habit, with short stems, broad leaves and steady clumping growth in a pot.
In a container, the plant gradually forms a short-stemmed clump. Older leaves sit lower around the base while new leaves emerge from the crown, creating a layered look as it matures.
Aglaonema 'Spring Red' care essentials
- Light: Place in bright filtered light or gentle morning sun. Direct midday sun can mark the coloured leaf tissue.
- Watering: Water when the top 3–5 cm of substrate feels dry. The pot needs to feel lighter before watering again.
- Substrate: Use a loose mix with coco coir, fine bark, perlite or pumice so the roots stay lightly moist with air around them.
- Drainage: Keep the plant in a pot with drainage holes and empty any standing water after watering.
- Temperature: Keep at 18–27 °C. Cold windowsills and draughts can cause dull, water-soaked patches on the leaves.
- Humidity: Normal indoor humidity is usually suitable. In very dry heated rooms, grouping plants or using a humidifier reduces crisping on new leaves.
- Feeding: Feed lightly every 4–6 weeks in spring and summer with diluted foliage-plant fertiliser.
- Repotting: Repot when roots fill the pot or the substrate breaks down, usually every 1–2 years.
- Grooming: Remove fully yellow lower leaves at the base so the crown stays open and dry.
- Propagation: Divide rooted basal shoots or root stem sections with at least one node in warm conditions.
Aglaonema 'Spring Red' leaf responses
- Soft yellow lower leaves: Check for a wet lower root ball, blocked drainage or a cover pot holding water.
- Brown dry tips: Check watering gaps, dry heated air and mineral build-up from strong fertiliser or hard water.
- Pale scorched patches: Move the plant away from direct sun through glass and leave damaged leaves until replacements grow.
- Drooping with damp substrate: Inspect the roots and allow the mix to dry further before the next watering.
Aglaonema 'Spring Red' safety
Aglaonema 'Spring Red' contains insoluble calcium oxalate crystals. The plant can irritate the mouth, throat and digestive tract if eaten, so keep it away from pets and small children. Wash hands after pruning if sap touches the skin.
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4.9 ★★★★★
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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