SKU: 4910729172
sunset jade plant

sunset jade plant Crassula Ovata Hummel's Sunset Tree – Beaultiful Desert Plants

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sunset jade plant Crassula Ovata Hummel's Sunset Tree – Beaultiful Desert PlantsCrassula Ovata Hummel's Sunset Tree * Ship Bare root (without pot and soil) Pot size is only notated for your reference. * If you are going plant your cactus or succulent in a pot, have it prepared beforehand with cactus mix soil (recommended), then water lightly. *If you going to plant it into the ground, ensure proper drainage *We Only ship Priority to ensure your Succulents plant will take between 2 to 3 days to arrive, we are not responsible for

Crassula Ovata Hummel's Sunset Tree

* Ship Bare root (without pot and soil) Pot size is only notated for your reference. * If you are going plant your cactus or succulent in a pot, have it prepared beforehand with cactus mix soil (recommended), then water lightly. *If you going to plant it into the ground, ensure proper drainage *We Only ship Priority to ensure your Succulents plant will take between 2 to 3 days to arrive, we are not responsible for any the shipping carriers are delays. * My Jades have really short roots, We reefed to the gallons that are planted in the add so you can see or estimate the actual size of the plant, do not expect to receive any Jade with long roots, That's why these trees are easy to Trim and give a shape as a bonsai tree and plant them in a shallow pot. * We try to ship our succulent plants as soon as we get the order is customer responsibility to be aware of the plant arrival also customers will get a notification by email. If the customer wants to delay or change the day of the shipment please contact us as soon as possible. *We take great care in the packaging of your plants, but unfortunately the same cannot always be said in how they are handled once they leave us .*Is the customer responsibility to purchase a (Heat pack) if the Succulent plant is ship to a cold area, we usually recommended it if the whether is 35* or lower, If you are purchasing a large succulent plant please make sure you buy enough heat pack to cover the plant (1 heat pack every 12") We are not responsible for damages to the Succulent plant if is NOT enough coverage of the heat pack, and if is delay by USPS, the heat only will keep the box warm for 72 hours. 

Ovata Hummel's Sunset Tree Scientific Name Crassula ovata ‘Hummel’s Sunset’ Common Names Golden Jade Tree Synonyms Crassula ovata ‘Sunset’ Scientific Classification Family: Crassulaceae Subfamily: Crassuloideae Genus: Crassula Description Crassula ovata ‘Hummel’s Sunset’ is a shrubby succulent up to 3 feet (90 cm) tall with leaves about the same size as the common Jade Plant (Crassula ovata). Unlike the green leaves of the species, the leaves of this cultivar take on spectacular hues with the upper portion of the leaves having a golden yellow color and the edges ochre, especially during the cooler months of the year. The late fall to winter flowers are white with a hint of lavender.

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SKU: 4910729172

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Carol
Phoenix, 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.
Los Angeles, 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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Verified Purchase
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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Verified Purchase
Richard Hackathorn
Bozeman, 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
A
Verified Purchase
Amazon Customer
Pawtucket, 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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