SKU: 85071684879

Iced Coffee Glass

$13.50 $15.00
Save 10%
No.2 Bestseller on lamcf.fr >

Pay in installments of $3.75 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Aug 20 - Aug 25

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

Iced Coffee GlassDon't miss out on this beautiful Iced Coffee Cup! Introducing the perfect companion for your morning routine the Lizzo inspired iced coffee glass. Made from high quality glass, this chic and trendy cup is designed to keep your favorite iced beverages cool and refreshing. The sleek design and sturdy construction make it ideal for both home and on the go use. Materials: Crafted from durable glass, this iced coffee cup is built to last for countless

Don't miss out on this beautiful Iced Coffee Cup!

Introducing the perfect companion for your morning routine - the Lizzo inspired iced coffee glass. 

Made from high-quality glass, this chic and trendy cup is designed to keep your favorite iced beverages cool and refreshing. 

The sleek design and sturdy construction make it ideal for both home and on-the-go use.  

Materials:
Crafted from durable glass, this iced coffee cup is built to last for countless uses. The transparent design allows you to showcase the vibrant colors of your favorite iced drinks, adding a stylish touch to your daily routine.

Care Instructions:
To maintain the condition of your iced coffee cup, we recommend hand washing with mild soap and water. Avoid using abrasive materials or harsh chemicals that may damage the glass.

With proper care, this cup will continue to be your go-to choice for enjoying your beloved iced beverages. Elevate your coffee experience with this must-have iced coffee cup!

The 16oz. Glass includes a bamboo lid and glass straw (cleaning brush included).

[CARE INSTRUCTIONS]
- Do not soak in water, Wash in Cold water
- Light soap
- Gently hand wash only, do not scrub vinyl
- Not dishwasher safe
- Do not microwave

[SHIPPING & PRODUCTION TIME]
*Production time is 1-2 business days.
*Shipping Time is 1-3 Business Days depending upon shipping selection.
*USPS First Class Mail or USPS Priority Shipping


** All items are handmade so there may be a slight difference in the appearance of vinyl positioning, color, etc. This Iced Coffee Cup is made with high-quality permanent vinyl.

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: 85071684879

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.4 ★★★★★
Based on 23 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
P
Verified Purchase
Par
Fort Morgan, 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
Houston, 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
Port Orchard, 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
Lexington, 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

recommand products

Monitor Audio Creator Series In-Ceiling - C3L-CP
749.50
Kaleidoscope Pouch
18.00
Monitor Audio Creator Series In-Wall - W1M
349.50
VINTAGE CHAMPION SWEATER (S)
19.95
Pivot Shuttle AMP'd
8997.50