SKU: 25371105782

Lasso & Boots Mini Urn Add Text, Small Shareable Urn, Infant Urn Personalized Handmade Custom Laser Engraved

Sale price$52.20 Regular price$58.00
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Ships within 48 hours · Estimated delivery Aug 20 - Aug 25

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Description

Lasso & Boots Mini Urn Add Text, Small Shareable Urn, Infant Urn Personalized Handmade Custom Laser EngravedOur Urns All of our urns are made from premium hardwood, no plywood or pressboard! The design shown in the photos will be engraved along with your custom text. We do not stain or paint any of our urns but apply several finishing coats of lacquer to show off the beautiful natural wood grain. Choose from 4 different woods (shown in the listing photos): Ambrosia Maple, Mahogany, Cherry, and Walnut. Please note that the wood grain will vary. Ambrosia

Our Urns

All of our urns are made from premium hardwood, no plywood or pressboard! The design shown in the photos will be engraved along with your custom text. We do not stain or paint any of our urns but apply several finishing coats of lacquer to show off the beautiful natural wood grain.
Choose from 4 different woods (shown in the listing photos): Ambrosia Maple, Mahogany, Cherry, and Walnut. Please note that the wood grain will vary.

  • Ambrosia maple wood varies greatly and can be very light with little variation or very dark with grain contrasts and wormholes.
  • Cherry wood is our second lightest wood and is fairly consistent.
  • Mahogany is a darker reddish wood.
  • Walnut wood is our darkest, please note that walnut wood may not show engraving as well as the other woods.

This urn measures approximately 3.5" long x 2.875" wide x 2.125" tall. The top of the box swings shut, and a magnet securely holds the lid closed. These boxes can hold small amounts of ashes, approximately ashes from 3lbs or less before cremation. You can purchase several boxes to split the ashes between boxes to give to multiple parties. A small plastic bag will be shipped inside all of our mini urns.

Features & Benefits:

  • Handmade in the USA
  • Each urn is handmade and engraved specifically for your loved one with their name, dates, and any other text you would like included. We take extra care to make sure they are honored respectfully, and their urn is personalized specifically for them.
  • Made from Premium Hardwood, no synthetic wood, no pressboard, no plywood, no MDF, etc., because of this, your urn is built to last honoring the memory of your loved one.
  • Every urn is unique, just like each piece of wood is unique!  Since we use real hardwood, no 2 trees are the same, so no 2 urns are the same!
  • Choose from any of our 4 hardwoods.

To Order

  1. From the drop-down box, choose your wood type
  2. From the drop down box, select your engraving option. The design shown in the first image will be engraved along with your text. 
  3. In the box provided, note the text you would like engraved on the top of your box. If you chose to engrave the front or bottom, please note the text you would like and specify the correct placement for each engraving in the appropriate box provided. (We do not have a specific limit on the number of characters, but keep in mind that the more characters you have, the smaller the font will be.) Please be sure to double-check all spelling and capitalization as we will engrave it EXACTLY as you have noted.

EXAMPLE:

Engraving Front: Tom Smith 4.10.58 – 8.24.18

Engraving Top: Your Wings Were Ready, Our Hearts Were Not

Engraving Inside Lid: We Love You

 

  1. From the drop-down box, choose which style font you would like for your engraving. A list of font choices is shown in the listing photos.
  2. Add the item to your cart.
  3. Leave any other info or requests in the Message box in your cart and proceed with checkout.

 

We select specific cuts for each urn, but if you have a specific preference, would like a lighter piece of walnut, an ambrosia maple piece with a lot of grain contrast, etc., please note your preference at check out and we will do our best to pick a piece to your request.

Shipping

Upon completion, this item is shipped via UPS or USPS for US deliveries, and USPS International Shipping 10-15 Day for International deliveries. International customers are responsible for any customs fees charged by their country before their order can be delivered. International customers will have to provide an email address and phone number for delivery.

Ambrosia Maple Wood known as Ghost Maple, also has varying color and grain. Although normally light, ambrosia maple wood is known for its dark grain contrasts, adding brown, gray, and bluish highlights to the wood. Please note the wood grains for all woods will vary from item to item, and the worm holes (a characteristic of Ambrosia Maple Wood) will vary from item to item.

 

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 25371105782

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4.7 ★★★★★
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Product Reviews
P
Verified Purchase
Par
Dallas, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Battle Creek, 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
A
Verified Purchase
Amazon Customer
Charlottesville, 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
West Palm Beach, 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
T
Verified Purchase
Tommy Jonsson
Lowell, 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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