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are snake plants deer resistant

are snake plants deer resistant Snake Plant-Green (Sansevieria trifasciata) – Urbane Eight Inc

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Description

are snake plants deer resistant Snake Plant-Green (Sansevieria trifasciata) – Urbane Eight IncThe Snake Plant Green, botanically known as Sansevieria trifasciata, is a classic indoor plant valued for its upright structure, rich green foliage, and exceptional durability. With its sword like leaves and architectural form, this snake plant green adds clean lines and visual interest to a wide range of interior spaces, from modern living rooms to home offices and bedrooms. One of the reasons the snake plant indoor variety remains so popular is its

The Snake Plant Green, botanically known as Sansevieria trifasciata, is a classic indoor plant valued for its upright structure, rich green foliage, and exceptional durability. With its sword-like leaves and architectural form, this snake plant green adds clean lines and visual interest to a wide range of interior spaces, from modern living rooms to home offices and bedrooms.

One of the reasons the snake plant indoor variety remains so popular is its ability to adapt to different environments. This easy care houseplant tolerates low light conditions while also performing well in bright indirect light, making it a flexible option for homes with varying light levels. Its slow, steady growth and upright habit help it maintain a tidy appearance without frequent pruning or adjustment.

This plant is especially well suited for people who want greenery without a demanding care routine. Sansevieria trifasciata stores water in its thick leaves, allowing it to handle periods of dryness better than many other houseplants. This drought tolerance makes it a dependable choice for busy households, offices, or anyone who prefers a more relaxed watering schedule.

In terms of care basics, the snake plant green prefers well-draining soil and infrequent watering. The soil should be allowed to dry out completely between waterings, as excess moisture can lead to root issues. It does not require high humidity and adapts well to typical indoor conditions. Bright indirect light encourages stronger growth, but the plant will remain healthy even in lower light settings, growing more slowly.

It’s important to note that while this plant is low maintenance, it is not pet-friendly. Sansevieria trifasciata is considered toxic to cats and dogs if ingested, and it should be placed out of reach in homes with pets. If pet safety is a concern, this plant is best reserved for areas inaccessible to animals.

For shipping and acclimation, snake plants are resilient and generally adjust well to new environments. Some minor cosmetic stress may occur during transport, but with proper placement and conservative watering, the plant typically settles in quickly. Allowing a short adjustment period before watering helps the plant recover and establish itself in its new space.

If you’re looking for a structured, reliable, and visually striking plant that thrives with minimal effort, the Snake Plant Green is a timeless addition to any indoor collection.

Sizing Description:

Medium: 6" / 15cm pot diameter

XX-Large: 10" / 25cm pot diameter 

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SKU: 60292467863

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4.6 ★★★★★
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Par
Port Orchard, 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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Verified Purchase
Richard Hackathorn
Chelsea, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
San Leandro, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
New York, 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
T
Verified Purchase
Tommy Jonsson
Massapequa, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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