SKU: 97498477375
song of india dracaena reflexa

song of india dracaena reflexa XL Dracaena Reflexa Song of India – Happy Houseplants

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Description

song of india dracaena reflexa XL Dracaena Reflexa Song of India – Happy HouseplantsWe love this plant for its gorgeous vibrant yellow colouration and unusual shape! Dracaena Reflexa Song of India is an unusual shape with multiple stems and so many leaves they almost look like palms. These are tough, drought tolerant plants with aggressive root systems that make excellent houseplants. Dracena plants are not very good at coping with wet conditions and really hate sitting in saturated soil and they do need enough room for their roots

We love this plant for its gorgeous vibrant yellow colouration and unusual shape! Dracaena Reflexa Song of India is an unusual shape with multiple stems and so many leaves they almost look like palms. These are tough, drought-tolerant plants with aggressive root systems that make excellent houseplants. 

Dracena plants are not very good at coping with wet conditions and really hate sitting in saturated soil and they do need enough room for their roots to spread out, so need to be placed in a large pot. If the roots do not have enough room the wonderful variegation may not be seen. 

The Dracena also likes bright, filtered light, which makes it a great indoor plant!  The more light the plant receives, the better the variegation of the foliage there will be. However, keep out of direct sunlight. Too little or too much light will result in leaf drop.

If you’re really careful and give this plant the care it needs then you may be rewarded with Dracena flowers!

Dracena makes a fantastic indoor plant gift. We’ll make it even more special by including a free, handwritten card with your order. All you have to do is note your message at the basket stage of delivery and we’ll take care of it for you. No giveaway paperwork either!

Dracena care level

These plants are not tricky to manage so are recommended if you have just a little experience of houseplants but you must be confident that you won't overwater and you have access to good light.

Where should I put this plant?

Dracena can take high or direct light.

How should I water this plant?

The Dracena plant likes to be watered only when the top 2 inches of soil are dry to the touch. During the summer months, you need to water regularly.

Should I feed this houseplant?

dracaena can be given a liquid feed once a month during the spring and summer months. Have a look at our vegan, organic plant food! (link)

Is this houseplant suitable if I have pets?

Like most dracaena varieties, it is mildly toxic to pets. The problems with toxicity come from ingestion but it takes a large amount for symptoms to occur. 

What size is this plant?

W40cm X H160cm

If you’d like to see a video we have made about general houseplant care, have a look here.

All our plants are supplied in a plastic nursery pot

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SKU: 97498477375

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Chelsea, 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
Natrona Heights, 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
Lowell, 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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Verified Purchase
Amazon Customer
Omaha, 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
K
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Kindle Customer
Cuba, 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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