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Description
Compulocks Wall Mount for iPad (10th Generation) - Black, SilverElevate your space with a security focused, stylish solution designed to maximize the impact of your iPad 10th Generation. The Compulocks Wall Mount for iPad (10th Generation) in Black and Silver blends rugged durability with a sleek two tone finish, making it ideal for high traffic environments such as retail storefronts, clinics, and industrial facilities. Built to protect devices while delivering effortless access for customers and staff, this
Elevate your space with a security-focused, stylish solution designed to maximize the impact of your iPad 10th Generation. The Compulocks Wall Mount for iPad (10th Generation) in Black and Silver blends rugged durability with a sleek two-tone finish, making it ideal for high-traffic environments such as retail storefronts, clinics, and industrial facilities. Built to protect devices while delivering effortless access for customers and staff, this mount transforms your iPad into a reliable point-of-service, information kiosk, or interactive display without sacrificing aesthetics or safety.
From the moment you install it, you’ll notice a thoughtful balance between form and function. The two-tone Black and Silver finish complements modern interiors, while the robust construction provides long-lasting performance in challenging settings. The design prioritizes ease-of-use for end-users and security for operators, reducing downtime and the risk of tampering. Whether used for digital signage, self-checkout, wayfinding, or patient check-ins, this wall mount delivers a seamless, professional experience that helps you elevate customer engagement and streamline operations.
- Secure, tamper-resistant locking system: Protects your iPad from theft and casual tampering in busy environments. The locking mechanism is integrated into a sturdy mounting bracket to deter tampering while keeping the device accessible to authorized staff and customers.
- Durable two-tone construction: A rugged metal framework finished in black and silver provides a tasteful, unobtrusive look that fits retail counters, medical reception areas, and industrial workspaces without drawing unwanted attention.
- Flexible orientation and ergonomic viewing: Designed to support portrait and landscape viewing with adjustable tilt and swivel capabilities, enabling staff to position the iPad for optimal interaction and accessibility for users of all heights.
- Cable management and power access: A thoughtfully integrated cable channel keeps charging and data cables neatly concealed, reducing clutter and trip hazards while preserving a clean, professional appearance.
- Quick deployment for diverse environments: Suits a wide range of use-cases—from point-of-sale and interactive kiosks to patient check-ins and digital signage—without complex customization or special tools.
Technical Details of Compulocks Wall Mount for iPad (10th Generation) - Black, Silver
- Compatibility: iPad 10th Generation (2022) 10.9-inch model with USB-C
- Finish: Black and Silver two-tone presentation
- Material: Metal construction (robust framework designed for daily use)
- Security: Tamper-resistant locking system to deter theft and unauthorized removal
- Mounting: Wall-mount bracket with included mounting hardware for secure installation
How to Install Compulocks Wall Mount for iPad (10th Generation)
- Identify a suitable mounting location with stable wall support and plan for cable routing to power and network sources where needed.
- Mark the wall for the mounting plate screws, ensuring level alignment to keep the iPad screen orientation correct from the user’s perspective.
- Attach the wall-mount plate to the wall using the supplied hardware, following the included instructions for drywall, masonry, or stud-based installations as appropriate.
- Attach the bracket or arm assembly to the mounted plate, then secure the locking mechanism to prepare for device placement.
- Insert the iPad into the bracket, secure it with the integrated lock, and route the charging/data cables through the concealed channel to maintain a tidy appearance.
- Power on the iPad and verify accessibility in both portrait and landscape orientations, adjusting tilt and swivel as needed to optimize user comfort and reach. Finally, test the locking feature to confirm security before welcoming users.
Frequently asked questions
-
Q: Is this mount compatible with the iPad 10th Generation?
A: Yes. The Compulocks Wall Mount is designed specifically for the iPad 10th Generation (2022 model) with USB-C, ensuring a secure fit and reliable access.
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Q: Can the iPad be used in portrait and landscape modes?
A: Absolutely. The mount offers adjustable orientation so you can switch between portrait and landscape to fit the application, whether for signage, checkout, or check-in processes.
-
Q: Is there a locking system to prevent theft?
A: Yes. The wall mount includes a tamper-resistant locking mechanism that secures the iPad in place, making it suitable for high-traffic environments where security is a priority.
-
Q: How is cable management handled?
A: The design provides an integrated cable channel that hides charging and data cables, helping maintain a clean, professional appearance and reducing tripping hazards.
-
Q: Is installation hardware included?
A: Yes. The kit includes mounting hardware suitable for common wall types and mounting scenarios, enabling a straightforward setup with minimal tools and time.
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4.1 ★★★★★
Based on 2028 reviews
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Product Reviews
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience.
If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference.
I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance:
The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals).
Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning.
Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time.
And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning.
There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s.
The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read.
There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning.
The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique.
Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry.
The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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