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Description
cybex green leaf Cybex Platinum® Car Seat Cloud T i-Size 0+ (45-87cm) PLUS Leaf Green – EvitasCybex Platinum Cloud T i Size (013 kg) PLUS Sepia Black An advanced infant car seat combining top tier safety, ergonomics, and premium comfort from birth. The Cloud T i Size sets new standards in comfort both in the car and on a stroller. Key Benefits Ergonomic recline position in the car for comfortable sleep Near flat position outside the car (travel system) 180 rotation (with Base T) for easy on and off boarding Advanced L. S. P. system for side
Cybex Platinum® Cloud T i-Size (0–13 kg) PLUS – Sepia Black
An advanced infant car seat combining top-tier safety, ergonomics, and premium comfort from birth. The Cloud T i-Size sets new standards in comfort – both in the car and on a stroller.
Key Benefits
- Ergonomic recline position in the car for comfortable sleep
- Near-flat position outside the car (travel system)
- 180° rotation (with Base T) for easy on and off-boarding
- Advanced L.S.P. system for side-impact protection
- XXL sun canopy with UPF50+ protection
Premium Comfort Without Compromise
The Cloud T i-Size features an innovative in-car recline that simultaneously adjusts the backrest and leg rest. This allows your baby to lie in a more natural position while keeping the head safely supported – ensuring unrestricted breathing and restful sleep.
Outside the car, the seat reclines to a near-flat position, providing maximum comfort when used on a stroller.
Optimal Breathability – PLUS Version
The PLUS version features advanced 3D mesh inserts that improve breathability by up to 6 times. This ensures reduced heat buildup and maximum comfort in all seasons.
Difference Between PLUS and Comfort
- PLUS: enhanced breathability (3D mesh), improved airflow, premium feel
- Comfort: classic fabric, same comfort level, without additional ventilation
- Both versions: identical safety, functionality, and ergonomics
Safety Without Compromise
The Cloud T i-Size meets the latest UN R129/03 (i-Size) safety standard, offering outstanding protection.
- Linear Side-impact Protection (L.S.P.) – up to 25% more protection in side impacts
- Energy-absorbing shell
- 3-point harness system
- Newborn inlay for optimal support
Easy to Use
- 180° rotation (with Base T) for easier handling
- Quick installation on ISOFIX base
- Installation possible with vehicle seat belt
- Seamless transition from car to stroller
Travel System – Total Mobility
Compatible with CYBEX strollers, allowing an effortless transition from car to stroller without waking your baby. The perfect solution for everyday mobility.
Usage
- Child height: 45 – 87 cm
- Child weight: up to 13 kg
- Age: from birth up to approx. 24 months
Dimensions
- Length: 645 – 750 mm
- Width: 440 mm
- Height: 380 – 600 mm
- Weight: 4.5 kg
Compatibility
- Base T
- Base Z2
- Summer Cover
- Sun Shade
- Insect Net
- Rain Cover
- SensorSafe 4-in-1 Kit Infant
- Snogga Mini 2
- Platinum Winter Footmuff Mini
Awards
Why choose Cloud T i-Size?
✔ Ergonomic recline even in the car
✔ Near-flat position on stroller
✔ Superior breathability (PLUS version)
✔ Latest i-Size safety standard
✔ Seamless compatibility with CYBEX strollers
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4.9 ★★★★★
Based on 1864 reviews
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Product Reviews
★★★★★ 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
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff!
If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017