SKU: 94072561067
full size ponytail palm

full size ponytail palm 2-3 Feet Ponytail Palm

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Description

full size ponytail palm 2-3 Feet Ponytail PalmWe love the playful Ponytail Palm that hails from the tropics in eastern Mexico. Its such a beauty that it was in high demand around Europe and then shortly after, all over the world. Also known as the Beaucarnea recurvata, the large Ponytail Palm is vocal about what it doesnt like. However, its also a sturdy houseplant that doesnt need to be watered too often, so that makes things nice. Besides its easygoing attitude, the plant brings a playful and

We love the playful Ponytail Palm that hails from the tropics in eastern Mexico. It’s such a beauty that it was in high demand around Europe and then shortly after, all over the world. Also known as the Beaucarnea recurvata, the large Ponytail Palm is vocal about what it doesn’t like. However, it’s also a sturdy houseplant that doesn’t need to be watered too often, so that makes things nice. Besides its easygoing attitude, the plant brings a playful and intriguing vibe that will embody the tropics. In the Palm’s natural jungle habitats, it can reach extremely tall heights, sometimes reaching approximately 30 feet tall in ideal conditions. Other names for this plant are the Elephant’s Foot and Bottle Palm. If the large size is too big, you can check out the medium Ponytail Palm we have. Hand-pick your own ponytail palm with Pafe Live!

Ponytail Palm Benefits

The Palm’s dense trunk can keep water inside, making the plant able to thrive for a while without a lot of water. It can bounce back quickly from neglect and will show you noticeable changes and signs when it’s going downhill. Besides that, the Ponytail Palm helps purify the air when it absorbs surrounding airborne toxins.

Tree or Palm?

Sike. It's actually not a palm at all, nor a tree, but it sure looks like one. The Ponytail Palm is a member of the Agave family, originates in the deserts, and is technically a succulent. The name can be quite deceiving and really only refers to its thick trunk and tree-like appearance. While most in the Agave family tend to grow very slowly, the Ponytail Palm breaks the mold and can reach larger sizes quicker.

Ponytail Waterfalls

Leaves on the Ponytail Palm really look like ponytails. The leaves spill outwards in a waterfall of ponytails that are long, and wirey, like strands of straight hair. Leaves will grow in dense groups that can also make it look like a cleaning mop. When the plant needs a little love and care, its ponytail leaves might begin to get brown. Check the soil and maybe try watering less. What’s the sunlight situation? Reach out to Pafe Plants and we can help set you on the right track.

Elephant Vibes

The Ponytail Palm reaches towering heights when it’s free to expand in its natural habitat. Due to its appearance and boundless growth, others have referred to the houseplant as Elephant’s Foot Tree because when the trunk reaches this bigger size, it looks like the leg and foot of an elephant. The base of the trunk becomes wider than the rest and paired with its muddied off-white color and wrinkled appearance really gives off the elephant leg vibes. Some trunk bases can grow as wide as 4 feet.

Now that you're an expert with the Ponytail Palm, order this plant by 7PM est to get this plant shipped out of our greenhouse in New Jersey tomorrow (if you are wondering the arrival time, check with the zipcode validator on top of the Add To Cart). Seriously, our plant shipping solution is truly protective and innovative to ensure your plants arrive safe and intact! In case you were still wondering, we WILL send out the EXACT plant that you picked out, just like that of a local nursery or garden center, except we have more and fresher plants to choose from and you can't find our PAFE fine ceramic planter options anywhere else other than our website :)

For any other questions or concerns, please don't hesitate to reach out to us at [email protected] or call/text (609)-968-7063!
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SKU: 94072561067

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Hashi Hanta
Waukegan, US
★★★★★ 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
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Carol
Chelsea, US
★★★★★ 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
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Cuba, 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
Draper, 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
Natrona Heights, 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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