SKU: 24956702438

Floral Checkers Black Phone Case

$20.19 $22.43
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Ships within 48 hours · Estimated delivery Aug 22 - Aug 27

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Description

Floral Checkers Black Phone CasePremium cases, unique designs to match your style with shockproof protection that will make your device look stylish and protected. Available for Apple iPhone, Samsung Galaxy S series & Google Pixel devices. Visit AcasoLondon on Etsy for more colours and designs. Glossy finish only. If you wish to proceed with Matte finish please leave a note or email us with your order number within 24h after purchase. Zero waste packing, all 100% recyclable. Made to

Premium cases, unique designs to match your style with shockproof protection that will make your device look stylish and protected. Available for Apple iPhone, Samsung Galaxy S series & Google Pixel devices.

Visit AcasoLondon on Etsy for more colours and designs.
Glossy finish only. If you wish to proceed with Matte finish please leave a note or email us with your order number within 24h after purchase.

♻️ Zero waste packing, all 100% recyclable.
📦 Made to order. Selection of 200+ original designs.
🍃 Sustainable production, made & designed in the UK.
✍️ Designs Human-Made. No Generative AI Used.

Please make sure you select the right model to avoid any returns. Not sure how? Go to Settings → Model Number. We're always trying to be as much sustainable as we can so we'd highly appreciate your help 🤲🏻

Choose from our two different styles - Snap (aesthetic) and Tough (aesthetic and protective):

SNAP CASE - These phone cases are designed for aesthetics. They're LIGHT & SLIM with the design fully wrapped around. It does NOT offer FULL COVERAGE as it is open on top & bottom. Simply snap the case on your mobile phone and flaunt your style.

TOUGH CASE - If you want a little more than just looks, go for this TWO PIECE phone case. It not only looks good but also offers FULL COVERAGE and it's super PROTECTIVE. The only downside is the inner rubber is only available in black.

⚠️ Important: Colours of the design may vary due to the printing process. Snap cases may come with small stains on the inside due to the printing process. This does not mean that it has been used.

📦 SHIPPING (excluding processing times which can take an extra 2-5 days)
- Standard UK Royal Mail delivery within 1-3 working days (no tracking).
- Standard US DHL/USPS or STAMPS delivery within 2-5 working days (with tracking).
- Europe delivery via Spring Global/La Poste or Post NL delivery within 5-8 working days (with tracking).
- Australia Standard Australia Post Stamps within 3-5 working days (no tracking).
- Canada via Asendia US and Japan via Korean Post might take up to 11 business days to be delivered (tracking available).

Fill our contact form or email us at [email protected] for more information.

📨 RETURN POLICY
Our products are labeled as "Custom" or "Personalised". As a small business with sustainability in mind, all our cases are made-to-order to avoid minimal production waste. Exchanges are still accepted in some cases but please make sure to select the right model as returns can automatically be rejected. All items must be returned within 14 days of the delivery date, to our UK studio. Customers are responsible for shipping costs.

📃 POLICIES
- If you notice any defects upon arrival such as print issues or damage, please provide photos along with your message within 7 days of receiving the item for replacements or refunds.
- In case of a refund, shipping costs are non-refundable.
- Very small spots/dots that do not affect the design will not be considered as damaged and are therefore, not eligible for a full refund or replacement. But a 30% partial refund and discount code will be provided.

For more please click here (FAQ) & Shop policies.

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: 24956702438

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4.8 ★★★★★
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Product Reviews
W
Walter Echo-Hawk, author of THE SEA OF GRASS.
Draper, 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
Bozeman, 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
Alexandria, 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
San Leandro, 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
Port Orchard, 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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