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Description
cybex aton isize Cybex Aton G2 Infant Car Seat w/ Load Leg BaseSpecifications: Age Range: From newborn up to approx. 18 months Child Height: max. 32" Child Weight: From 4 lbs. up to 30 lbs. Group: Infant Car Seat GREENGUARD Gold Certified Complies with new FMVSS213 side impact and frontal impact standard safety standards Travel Light. Travel Safe. The CYBEX Aton G2 infant car seat exceeds expectations by prioritizing safety and comfort from day one. It features advanced Linear Side Impact Protection and a Load
Specifications:
- Age Range: From newborn up to approx. 18 months
- Child Height: max. 32"
- Child Weight: From 4 lbs. up to 30 lbs.
- Group: Infant Car Seat
- GREENGUARD Gold Certified
- Complies with new FMVSS213 side impact and frontal impact standard safety standards
Travel Light. Travel Safe.
The CYBEX Aton G2 infant car seat exceeds expectations by prioritizing safety and comfort from day one. It features advanced Linear Side-Impact Protection and a Load Leg Base designed to absorb and minimize collision forces acting on the child in the event of a crash. Intuitive features include SensorSafe Smart Technology, which alerts you to unsafe conditions like temperature spikes, and the SafeLock Belt Tensioner, which ensures correct and easy installation every time. Its lightweight design ensures effortless mobility when transporting your little one from car to stroller. The Aton G2 delivers the thoughtful safety and convenience you expect.
- GOLD CERTIFIED: Ensures lower chemical emissions; contributes to healthier air quality around your child.
- ENGINERRED FOR PROTECTION: Our child restraint systems are fully compliant with FMVSS 213b side and frontal-impact safety standards—meeting the highest safety standards for crash protection.
- OPTIMAL BREATHABILITY: Keep your child comfortable in all seasons thanks to all-around air ventilation with the perforated shell and mesh fabrics.
- 11-POSITION ADJUSTBLE HEADREST: The 11-position height-adjustable headrest features an integrated harness guide that automatically adjusts the harness length as the headrest is raised, eliminating the need for rethreading.
- OVERSIZED SUN PROTECTION: Providing maximum sun protection, the UPF50+ oversized canopy elegantly tucks away when not in use.
- FIRE-RETARDANT CHEMICAL-FREE FABRIC: The FR-free materials used in CYBEX car seats pass all regulatory requirements while containing no additional added FR-chemicals.
- TRAVEL SYSTEM COMPATIBLE: Easily attaches to your CYBEX stroller creating a convenient and versatile travel system designed for life's adventures on the go.
- EASY-IN BUCKLE PAD: Keeps the buckle forward and out of the way, making it easier to secure your child.
- FAA-COMPLIANT: Approved for use on aircraft without the base, offering a safe, convenient solution for flying families.
- EASY TO CLEAN: The seat fabrics are crafted for easy care, enabling hassle-free machine washing to keep the car seat fresh and clean with minimal effort.
- DIMENSIONS: 26.1"L X 17.5"W X 14.7"H
- WEIGHT: 9 lbs
Care Instructions:
- Machine wash separately in cold water, delicate cycle. No bleach. Drip Dry. Do Not Iron.
Compatible with:
- All Cybex Strollers
- Aton G2 Load Leg Base
- UppaBaby Strollers
- Veer Strollers and Wagons
- SIlver Cross Strollers
- Bugaboo Strollers
- Peg Perego Strollers
- Bumbleride Strollers
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- Free Standard Shipping on $100+ Orders to the USA.
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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]
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4.7 ★★★★★
Based on 8 reviews
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Product Reviews
★★★★★ 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
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper.
As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture.
So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money.
The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018