SKU: 17566532947
anthurium red bird

anthurium red bird Anthurium 'Big Red Bird' – Dark Red Bird's Nest Anthurium

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Description

anthurium red bird Anthurium 'Big Red Bird' – Dark Red Bird's Nest AnthuriumAnthurium Big Red Bird Thick, wavy leaves rise from the central base of Anthurium Big Red Bird, building a broad birds nest rosette over time. Red toned veins or flushing add warm colour detail across the firm green foliage. The plant gains width as the rosette expands, with foliage spreading outward from the base. A stable pot helps balance the leaf mass as mature leaves gain size and weight. Foliage traits on Anthurium Big Red Bird Growth form: Self

Anthurium ‘Big Red Bird’


Thick, wavy leaves rise from the central base of Anthurium ‘Big Red Bird’, building a broad bird’s-nest rosette over time. Red-toned veins or flushing add warm colour detail across the firm green foliage.

The plant gains width as the rosette expands, with foliage spreading outward from the base. A stable pot helps balance the leaf mass as mature leaves gain size and weight.



Foliage traits on Anthurium ‘Big Red Bird’


  • Growth form: Self-heading bird’s-nest habit with leaves arranged from a central base.
  • Leaf texture: Thick, firm foliage with a leathery feel.
  • Leaf edge: Wavy to undulating margins that become more noticeable as leaves size up.
  • Colour detail: Red veins or red flushing can show under suitable filtered light.
  • Leaf emergence: New leaves rise from the central crown before spreading outward.
  • Pot behaviour: Mature plants benefit from a stable pot that balances the broad rosette.


Rosette growth and indoor space


The foliage of Anthurium ‘Big Red Bird’ expands outward from the centre, so the plant needs more horizontal space as it grows. Leaves can become broad and weighty, and the pot should remain stable when the rosette leans toward the light.

Brighter filtered light can bring out stronger red tones, but the leaves should be protected from hot direct sun. A warm, sheltered indoor position with room around the rosette keeps the leaf edges from rubbing and reduces trapped moisture between crowded leaves.



Care for Anthurium ‘Big Red Bird’


  • Light: Keep it in bright filtered light. Direct sun through glass can scorch the thick leaves, especially during hot hours.
  • Water: Water thoroughly, then allow the upper layer of the mix to dry before the next watering.
  • Humidity: Moderate to higher humidity keeps leaf edges cleaner, especially while new leaves are opening from the centre.
  • Temperature: Warm, steady indoor temperatures suit the rosette best. Avoid cold drafts around the base.
  • Substrate: Use a chunky, breathable Anthurium mix so the central root mass does not stay waterlogged.
  • Pot stability: When the plant becomes wide, use a pot heavy enough to hold the rosette upright.
  • Outdoor summer care: In warm weather it can sit in a sheltered bright spot outside once acclimated, but it should come back indoors before nights turn cool.


Common issues with Anthurium ‘Big Red Bird’


  • Brown margins: Usually linked to dry air, inconsistent watering, direct sun or a root ball that has dried too far.
  • Curled leaves: Check for a dry root ball, compacted mix or heat stress near glass.
  • Reduced red colour: Lower light can soften the red flushing, while excessive sun can damage the leaf surface.
  • Root stress: Heavy wet substrate around the central base can lead to yellowing and soft roots.
  • Older leaf pests: Inspect the undersides and leaf bases for scale, mites or mealybugs.


Safety for Anthurium ‘Big Red Bird’


Anthurium ‘Big Red Bird’ should be kept away from pets and small children. Chewed leaves or stems can release calcium oxalate crystals that irritate the mouth and digestive tract.



Name etymology and background


The genus name Anthurium comes from Greek roots meaning flower and tail, referring to the spadix. The cultivated name ‘Big Red Bird’ refers to the broad bird’s-nest rosette, red-toned foliage detail and thick wavy leaves spreading from a central base.

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Boise, US
★★★★★ 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
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Par
New York, US
★★★★★ 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
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Richard Hackathorn
Lexington, US
★★★★★ 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
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Amazon Customer
Birmingham, US
★★★★★ 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
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Kindle Customer
Draper, US
★★★★★ 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

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