SKU: 4316369774
britax car seats on sale

britax car seats on sale Britax Poplar S Convertible 2-in-1 Car Seat with ClickTight

$23.31 $25.90
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Description

britax car seats on sale Britax Poplar S Convertible 2-in-1 Car Seat with ClickTightReady to go with room to grow! The Poplar S convertible car seat takes the stress out of moving to the next car seat stage thanks to the patented ClickTight installation and slim SpaceSaver design. This convertible car seat accommodates up to 50 lbs when installed rear facing, allowing your child to ride rear facing longer. It easily switches to a forward facing car seat as your child grows, accommodating up to 65 lbs. ClickTight technology provides

Ready to go with room to grow!

The Poplar® S convertible car seat takes the stress out of moving to the next car seat stage thanks to the patented ClickTight® installation and slim SpaceSaver™ design. This convertible car seat accommodates up to 50 lbs when installed rear-facing, allowing your child to ride rear-facing longer. It easily switches to a forward-facing car seat as your child grows, accommodating up to 65 lbs.

ClickTight technology provides fully open seat belt paths that are clearly labeled and easy to access, and the automatic tensioner takes care of the tightening. This baby car seat lets you easily adjust the 14-position no-rethread harness and headrest to help create the perfect fit. 

The premium inserts also help provide refined comfort and a tailored fit. The ReboundReduce™ stability bar easily attaches to the car seat when installed rear-facing, helping to minimize movement in the event of a crash. Slim outside and spacious inside, this convertible car seat features 17-inch SpaceSaver™ technology designed to fit 3-across* without compromising on trusted Britax® safety and premium comfort.

The rear-facing car seat includes a patented SafeCell® crumple zone and a V-shaped top tether to help manage crash energy. Carbon steel frame and belt paths provide strength and stability. The car seat cover fabrics are naturally flame-retardant with no added FR chemicals, and they’re safe to machine wash and dry time and again.

*Britax cannot guarantee 3-across installation fitment in all vehicles

  • Convertible Design: Works as a rear-facing car seat with the infant insert and converts to a forward-facing car seat as your child grows 
  • Fit: Rear-facing 5-50 lbs; Forward-facing 30-65 lbs. *Due to regulatory changes, weight and height ranges may vary by model. Always follow the weight and height specifications listed in your product's user guide and on your product labels. 
  • ClickTight® Installation: With ClickTight, you know it’s right in just 3 easy steps: open, thread and buckle, click it closed 
  • ReboundReduce™ Stability Bar: Carbon steel bar easily attaches to the car seat when installed rear-facing to minimize the movement in the event of a crash; must be removed when installed forward-facing 
  • SpaceSaver™ Design: Slim 17" seat is designed to fit 3-across without compromising on trusted Britax® safety and premium comfort. *Britax cannot guarantee 3-across installation fitment in all vehicles 
  • Infant Insert: Specifically helps to properly position newborns
  • 5-Point Safety Harness: Helps evenly distribute crash energy to keep your child safe 
  • Patented V-Shaped Top Tether: Has 2 connection points to help slow and reduce forward movement in a crash; easily stores away when not in use 
  • Easy-Read Level Indicators: The 6-position and quick-adjust recline helps you find the most comfortable fit for your child and the best fit for your vehicle 
  • Naturally Flame-Retardant Fabrics: With no added FR chemicals 
  • SafeCell® Technology: Acts as a crumple zone, helping to keep crash energy away from your child 
  • Easy-Adjust Harness: The 14-position no-rethread harness and headrest move to help create the perfect fit 
  • Carbon Steel Frame: Reinforces the seat structure to help keep it sturdy and stabilized 
  • Flip-Forward Buckle Pad: Stays out of the way for easy boarding 
  • Washer and Dryer-Friendly: No more hand-washing or waiting for the cover to air-dry; just quick and easy cleanup, so you can keep moving 
  • Dishwasher-Safe Cup Holders: The 2 cup holders slide in and out for easy cleaning between rides 

Usage

  • Newborn to 50 lbs.

Specifications 

  • Dimensions: 23.5"H x 20.5"D x 17"W
  • Product weight: 28 lbs.
Shipping Notes
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SKU: 4316369774

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4.2 ★★★★★
Based on 24 reviews
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William P Ross
Fort Morgan, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Louisville, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Draper, US
★★★★★ 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
M
Verified Purchase
mackster
Massapequa, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Lake Worth, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 25, 2018

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