SKU: 57848054740
grad photo cards

grad photo cards Graduation Announcement Card Template for Senior & High School Grads | Class of 2023 Invitation, Yearbook, Photo Card Tribute, Canva

Sale price$18.62 Regular price$20.69
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Description

grad photo cards Graduation Announcement Card Template for Senior & High School Grads | Class of 2023 Invitation, Yearbook, Photo Card Tribute, CanvaCreate stylish announcement cards quickly with this editable template. Simply drag and drop your photos in and create a your graduation card ad in minutes! Comes with 2 pages which you can use for a double side card! Personalize the template in Canva for your photos, name, school and add a tribute or quote! BUNDLE Includes: Graduation Announcement Card Template Front AND Back cover templates included Drag and drop your photos in! Personalize with your

Create stylish announcement cards quickly with this editable template. Simply drag and drop your photos in and create a your graduation card ad in minutes! Comes with 2 pages which you can use for a double-side card!

Personalize the template in Canva for your photos, name, school and add a tribute or quote!

🎓 BUNDLE Includes: 🎓
✔ Graduation Announcement Card Template
✔ Front AND Back cover templates included
✔ Drag and drop your photos in!
✔ Personalize with your message or a dedication
✔ Add an inspirational quote
✔ Includes your class year and add your school/college name
✔ Size: 5x7"
✔ Available to edit in Canva - you can drag, drop and move elements easily
✔ Fully EDITABLE! Edit colors, fonts, text and photos to suit you!
✔ Edit on your laptop/tablet or on your phone directly!
✔ Edit with unlimited revisions - use the template over and over again

Elevate your Graduation Announcement!


💲SAVE 5️⃣0️⃣ % off with our link (copy & paste)💲
🔗 https://mailchi.mp/61c78c6de099/lets-connect 🔗


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~*~ What’s included ~*~

After buying, you will receive a link from Etsy to download a PDF with instructions and the link to your Graduation Announcement Card Template!

* Please note stock photos are not included. Please note that this template is editable in Canva, NOT in photoshop.
** Please note that a CanvaPro account may be required for some photos/elements in the design however you are welcome to replace these with the large selection of free elements within Canva or try their free trial. TWO versions of the template have been provided: One using a premium font, and one using a similar font under Canva's free plan.
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~*~ THIS IS A DIGITAL PRODUCT ~*~

Digital means its ready to download straight away after buying! No waiting, and no shipping fees. Purchase once and its yours forever!

*No physical product will be shipped, frame and props are not included*

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~*~ How to order ~*~

Its as easy as 1,2,3!
1. Buy,
2. Download,
3. Edit and enjoy!

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~*~ Refunds & Terms ~*~

Every digital and instant download purchase is non-refundable, as there is no way for us to tell whether you have printed the design. If you have any questions or concerns about your order, please feel free to reach out to us anytime.

Please note that a CanvaPRO account may be required for some photos/elements in the design however you are welcome to replace these with the large selection of free elements within Canva or try their free trial

You may print and design in your home, business, or a public place but may not resell, giveaway as a freebie, reproduce, distribute by electronic means or profit in any way from the design.

Please remember that colors may vary slightly when printed, due to monitor calibration settings, printout settings and material type

All additional props such as frames, items on display and non-incorporated writing are just for illustrative purposes and are not included within the sale.

We are not affiliated or associated with Canva.com in any way.
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Copyrighted by Creative Jam Co 2021
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SKU: 57848054740

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4.7 ★★★★★
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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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 14, 2019
C
Verified Purchase
Carol
Fort Morgan, 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
W
Walter Echo-Hawk, author of THE SEA OF GRASS.
Chelsea, 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
Lowell, 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
Boise, 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

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