SKU: 47784139029
evenflo pivot black

evenflo pivot black Evenflo Pivot Xplore Stroller Wagon Infant Car Seat Adapter

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Description

evenflo pivot black Evenflo Pivot Xplore Stroller Wagon Infant Car Seat AdapterThe Evenflo Pivot Xplore? Infant Car Seat Adapter makes the transition from car to the wagon quick and easy to get you rolling with two kids in tow. Simple in and out functionality without any tools to get you on your way. Just slide and click the adapter onto the Pivot Xplore frame and then place in your car seat with ease. Remove the adapter with a quick push button release. Whether your baby wants to face you or face the world, this modular Infant

The Evenflo® Pivot Xplore? Infant Car Seat Adapter makes the transition from car to the wagon quick and easy to get you rolling with two kids in tow. Simple in-and-out functionality without any tools to get you on your way. Just slide and click the adapter onto the Pivot Xplore frame and then place in your car seat with ease. Remove the adapter with a quick push button release. Whether your baby wants to face you or face the world, this modular Infant Car Seat Adapter is reversible to be forward-facing or parent-facing. Whether you want to push or pull your Pivot Xplore, just flip the wagon’s handle without even removing the car seat. Elevated placement of the car seat with adapter still allows space for a second child rider to sit comfortably in the wagon along with all your child care necessities.

For 100 years, Evenflo has been a trusted source of precision-manufactured, high-quality child-safety equipment engineered for real-life situations. Additionally, all Evenflo car seats and strollers include ParentLink customer-service perks, like real-time chat and free live video installation support.

The Pivot Xplore Infant Car Seat Adapter is compatible with the Pivot Xplore All-Terrain Stroller Wagon.

  • FLEXIBILITY ON-THE-GO: With infant in tow, quickly and easily transition your infant car seat from car to the Evenflo® Pivot Xplore? using the car seat adapter. The elevated level keeps baby close while still leaving plenty of room for a second rider below.
  • REVERSIBLE: The modular design allows for both parent-facing and forward-facing options in push or pull modes.
  • EASY ATTACHMENT: No tools required, just click the adapter to the frame. Then place car seat with ease. Simple and safe push button release removes the adapter.
  • COMPATIBILITY: LiteMax Infant Car Seat or SafeMax ?Infant Car Seat and Evenflo Gold SensorSafe SecureMax Smart Infant Car Seat
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SKU: 47784139029

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4.3 ★★★★★
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William P Ross
Phoenix, 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.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Grantham, 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.
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Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Port Orchard, 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
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mackster
Los Angeles, 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.
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Reviewed in the United States on May 15, 2018
S
Verified Purchase
Stergios Papadimitriou
Pawtucket, 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.
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Reviewed in the United States on August 25, 2018

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