SKU: 89658062651

STR-169220-AGS Silver Ticket, 220", 4K / 8K Ultra HD & HDR Ready, 16:9 Cinema Format, (6 Piece Fixed Frame) Projector Screen, AGS Dark Grey Material

$722.23 $802.48
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Description

STR-169220-AGS Silver Ticket, 220", 4K / 8K Ultra HD & HDR Ready, 16:9 Cinema Format, (6 Piece Fixed Frame) Projector Screen, AGS Dark Grey MaterialDescription Product is a 6 piece 220" Diagonal 16: 9 fixed frame screen and is made to mount on a wall. The Silver Ticket Products 220" 16: 9 Fixed Frame projection screen offers powerful performance for the price. Materials Available Matte White, Grey, HC Grey, Silver, Woven Acoustic. Please see other product pages for screen materials. This screen features real projection screen material, not a sheet. AGS stands for "Advanced Dark Grey". The

Description

Product is a 6 piece 220" Diagonal 16:9 fixed frame screen and is made to mount on a wall.

The Silver Ticket Products 220" 16:9 Fixed Frame projection screen offers powerful performance for the price.

Materials Available - Matte White, Grey, HC Grey, Silver, Woven Acoustic.  Please see other product pages for screen materials.  

This screen features real projection screen material, not a sheet. AGS stands for "Advanced Dark Grey". The material is a Dark Grey Reflective stretchy, high-quality vinyl at  0.95 gain that is designed for watching movies. Wait until you see the colors! There is no resolution lost at any angle. 

This screen truly assembles much faster than any other brand available! Forget about tucking the screen material into the frame - the Silver Ticket Tension Rod System saves you time and frustration. How does the material connect to the frame? Each side of the material has a pocket. A rod slides through the pocket to attach to the frame. This saves you time during assembly, but also removes any puckers or wrinkles in the viewing surface. The rod holds the material perfectly square all around the whole frame. This pocket and rod system remains hidden behind the frame so you only see the movie.

Forget about tricky installations - this fixed frame screen mounts on the wall much like a large picture frame. The sturdy aluminum frame is WRAPPED, not flocked, with a light absorbing black velvet fabric to absorb over-projected light so you don't have to be a professional when aligning your projector.

While other brands use square tubing in their frames, this screen is built with durable extruded aluminum that contours down to the projected image so you won't have any ugly shadow on the image area.

Join the thousands of satisfied customers -- buy Silver Ticket 220 inch,16:9 today!

 

USEFUL LINKS: 

Visual Installation Guide
Instruction Manual (.PDF)
Product Warranty

Product Details

SKU STR-169220-AGS
Category Projection Screen
Size 220
Style AGP Dark Grey 
Format 16:9 Format
Type Fixed Screen

Technical Specifications

Viewing Diagonal 220"
Viewing Width 192"
Viewing Height 108"
Format 16:9
Total Width 200"
Total Height 116"
Frame Width 3.875"
Frame Depth 1.25"
On-Axis Gain 0.95
Half Gain Off-Center Viewing Angle 80°
Half Gain Cone Viewing Angle 160°
Black-Backed Material No
Active 3D Compatible Yes
Passive 3D Compatible No
Recommended For 4K Yes
Flame Resistant Yes
Mildew Resistant Yes
Mild Detergent Washable Yes
Product Weight 92 lbs
Shipping Weight 99 lbs
Shipping Length 121"
Shipping Width 10"
Shipping Height 11"
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: 89658062651

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4.7 ★★★★★
Based on 26 reviews
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Product Reviews
P
Verified Purchase
Par
Lexington, 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
Massapequa, 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
Alexandria, 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
Massapequa, 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
Omaha, 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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