SKU: 72154997520

Natural Type A Multi-Color Jadeite Bangle | 57.1mm Brown, Faint Green & Lavender Tricolor Jade Bracelet (12.27g, 4.0x6.0mm (Slight Internal Line)

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Description

Natural Type A Multi-Color Jadeite Bangle | 57.1mm Brown, Faint Green & Lavender Tricolor Jade Bracelet (12.27g, 4.0x6.0mm (Slight Internal Line)The Painted Landscape: Type A Tri Color Earth & Pastel Jadeite Bangle Step into a realm of natural artistry and minimalist luxury with this extraordinary Type A Tri Color Jadeite Bangle. Weighing an incredibly light, feather weight 12. 27g and featuring a highly desirable 57. 1mm inner diameter, this bangle is a stunning showcase of jadeites fascinating, multi hued composition. It features a poetic interplay of earthy tones and soft pastels, offering

🎨 The Painted Landscape: Type A Tri-Color Earth & Pastel Jadeite Bangle

Step into a realm of natural artistry and minimalist luxury with this extraordinary Type A Tri-Color Jadeite Bangle. Weighing an incredibly light, feather-weight 12.27g and featuring a highly desirable 57.1mm inner diameter, this bangle is a stunning showcase of jadeite’s fascinating, multi-hued composition. It features a poetic interplay of earthy tones and soft pastels, offering a thoroughly unique, modern talisman for the contemporary collector.

🍁 The "Three Blessings" Palette: Earth, Mint & Violet

Formed over millions of years deep within the earth, this bangle acts as a miniature abstract landscape painting, naturally blending three distinct color profiles:

  • The Warm Earth-Brown: A bold, rich, and deeply grounding honey-brown vein that forms a striking anchor on the wrist. In jade culture, natural brown symbolizes stability, endurance, and anchored wealth.

  • The Faint Green: Soft, whisper-light strokes of refreshing mint green that bring a touch of youthful vitality and organic growth to the piece.

  • The Faint Lavender: Ethereal, misty undertones of soft violet (Ziluolan) that weave quietly through the stone, representing high status, nobility, and spiritual peace.

📐 Specifications & Dimensions

  • Material: 100% Genuine, Untreated Type A Natural Jadeite (Fei Cui)

  • Weight: 12.27 grams (An exceptionally rare, ultra-light profile that offers maximum all-day comfort)

  • Inner Diameter: 57.1mm (A highly sought-after, versatile mid-size)

  • Bangle Profile: 4.0mm Width x 6.0mm Thickness * The Sleek Profile Advantage: Moving away from heavy, traditional cylindrical bangles, this piece features a highly refined, slender, and flat-inner wall silhouette. It sits elegantly close to the skin, making it incredibly chic and effortless to wear.

  • Natural Stone Note: Features a slight internal line (inner stone grain), a completely natural characteristic of genuine, untreated Type A jadeite that does not affect the structural integrity or outer smoothness of the bangle, standing as proof of its organic origin.

🔮 The Symbolism: The Circle of Complete Harmony

The continuous, unbroken loop of a jade bangle holds profound spiritual meaning:

  • The Shield of Protection: Traditionally worn as a personal guardian amulet, a jade bangle absorbs the wearer's daily stress, warded off external negativity, and balances the body's natural energy field.

  • The Harmony of Three: The fusion of brown, green, and lavender represents the beautiful alignment of material wealth, thriving vitality, and deep mental tranquility, ensuring your life's path flows smoothly.

✨ Why It’s a Modern Masterpiece

Feather-Light Daily Luxury: Because it weighs just 12.27g, this is the ultimate signature daily bangle. You will completely forget you are wearing it, experiencing none of the heavy clanking or wrist fatigue associated with traditional, chunky jade bangles.

An Effortless Wardrobe Accent: Thanks to its multi-colored palette and delicate 4mm width, this bangle is a complete style chameleon. It looks incredibly sophisticated paired with corporate blazers, brings an artisanal edge to casual white linen, and layers beautifully next to a fine gold or silver chain bracelet.

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Remarks: All Natural Crystal, Wood and Gemstones may have impurities, naturally occurring line or rough unless higher end stated close to perfect or perfect. Small items like accessories will come with box or pouch. Display items will be in bubble wrap.

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SKU: 72154997520

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Battle Creek, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
P
Verified Purchase
Par
Los Angeles, 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
R
Verified Purchase
Richard Hackathorn
Pawtucket, 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
Whiting, 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
Boise, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026

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