SKU: 41969443796

32 Polynesian Turtle Brushes for Procreate, Tattoo Stamps for iPad & iPad Pro

Sale price$10.80 Regular price$12.00
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Description

32 Polynesian Turtle Brushes for Procreate, Tattoo Stamps for iPad & iPad Pro32 Turtle Brushes for Procreate Enhance your creativity with these detailed turtle designs in traditional Polynesian, realistic, tribal, and modern styles. Perfect for creating unique tattoo designs that will impress your clients. This product includes both a private and small commercial license, so you can use these brushes in your custom tattoo work. Features: 32 original turtle brush stamps (. brushset format), including both outline and shading

32 Turtle Brushes for Procreate - Enhance your creativity with these detailed turtle designs in traditional Polynesian, realistic, tribal, and modern styles. Perfect for creating unique tattoo designs that will impress your clients.

This product includes both a private and small commercial license, so you can use these brushes in your custom tattoo work.

Features:

  • 32 original turtle brush stamps (.brushset format), including both outline and shading versions
  • 1 small commercial license (.pdf) for custom tattoo designs
  • 1 installation guide (.pdf) in English

Compatibility Requirements:

  • iPad Pro or iPad
  • Apple Pencil (or stylus that supports pressure sensitivity)
  • Procreate app, version 5.0 or later

Convenient for Tattoo Artists: These brushes are ideal for creating stencils and saving time during the tattoo design process.

Download Instructions: After purchasing, you'll receive a download link via email. If the link doesn't arrive within 2 hours, please message me, and I'll ensure you get the files by email.

Additional Information:

  • Instant download available after payment confirmation
  • No returns, exchanges, or cancellations, but feel free to reach out if there's an issue with your order.
  • Check the store's terms of sale for more details.

SMALL COMMERCIAL LICENSE

+++ PERMITTED Usage:

  • Products can be used to create unlimited projects and/or products (both physical and digital) for personal and commercial use
  • You are allowed to create digital end products that you sell. In all cases, the product that you create must have distinctive new features that will create an entirely new product that won’t compete with the original product. Combining multiple downloads into one product is not enough. E.g., if you download a turtle brush to create a Polynesian tattoo template, you can sell that flattened template. If you download a turtle brush and create a card with just that design without making significant modifications to the original product, this is prohibited.
  • Products can be used for Print on Demand (POD). The end product must not compete with the original product.
  • This license is valid perpetually
  • This license is valid worldwide
  • No attribution is required

+++ PROHIBITED Usage:

  • It is prohibited to convert illustrations in vector graphics and sell them as a product.
  • You are never allowed to resell, share, re-distribute, or otherwise transfer items downloaded to other third parties.
  • You are never allowed to modify downloaded digital brushes and use them as new digital brushes.
  • You are never allowed to convert illustrations and digital brushes to other formats and sell them as new brushes for other software providers.
  • You are allowed to create Digital End Products that you sell. In all cases, the product that you create must have distinctive new features that will create an entirely new product that won't compete with the original product. Combining multiple downloads into one product is not enough. E.g., if you download turtle brushes to create a Polynesian tattoo template, you can sell that template. If you download a turtle brush and create a card with just that design without making significant modifications to the original product, this is prohibited.
  • Items or end products cannot be registered as a trademark in any territory. This license gives you non-exclusive rights. Other users can download and use the products as well, so you cannot claim sole ownership.
  • This license cannot be re-sold or transferred to any third party.
  • Graphics and patterns cannot be used as a library inside another tool, platform, or application. For example, if you are developing a tool that allows users to create designs, you cannot embed the products that you downloaded as a resource for these templates, e.g., letting users pick their own tattoo stencils.

 

created by tattoo artist: Rina Jonson Instagram for the PROCREATE app.

In seconds, you can create an original word on any background or body part, to draw your clients' projects.

Please note that these Figures are protected by copyright and may not be resold under penalty of legal action.

 

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: 41969443796

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4.7 ★★★★★
Based on 14 reviews
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Product Reviews
P
Verified Purchase
Par
Charlottesville, 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
Lowell, 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
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
T
Verified Purchase
Tommy Jonsson
Carnegie, 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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