SKU: 36815433969
bob monahan uppababy

bob monahan uppababy Uppababy Minu Duo Stroller Ada, Sandstone Melange | Chestnut Leather

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Description

bob monahan uppababy Uppababy Minu Duo Stroller Ada, Sandstone Melange | Chestnut LeatherThe Uppababy Minu Duo Lightweight Stroller is the ultimate travel companion for growing families, designed for parents of twins or siblings of different ages. Whether youre strolling through the park or navigating tight indoor spaces, this from birth, side by side stroller offers two full size, spacious seats that each support up to 50 lbs. Its narrow 27. 8 width fits through standard doorways, and the one hand, one step fold makes transporting and

The Uppababy Minu Duo Lightweight Stroller is the ultimate travel companion for growing families, designed for parents of twins or siblings of different ages. Whether you’re strolling through the park or navigating tight indoor spaces, this from-birth, side-by-side stroller offers two full-size, spacious seats that each support up to 50 lbs. Its narrow 27.8” width fits through standard doorways, and the one-hand, one-step fold makes transporting and storing the Minu Duo effortless—perfect for busy parents on the move.
Built with versatility in mind, the Minu Duo is compatible with Uppababy’s Aria and Mesa infant car seats using adapters (sold separately), allowing seamless transitions from car to stroll. Each seat features integrated foot barriers, adjustable footrests, and a no-rethread 5-point harness optimized for infants. Uppababy, a brand trusted for quality and innovation, is dedicated to making baby gear that’s intuitive, durable, and travel-friendly—so families can explore together without compromise.

UPPAbaby®, founded in Massachusetts in 2006 by husband-and-wife team Bob and Lauren Monahan, creates premium strollers, car seats, and travel systems that blend style, functionality, and safety. Inspired by real-life parenting needs, UPPAbaby designs high-quality, easy-to-use gear with modern aesthetics and innovative features. Built to grow with families, their products offer exceptional comfort, adaptability, and durability. Explore UPPAbaby® at ANB Baby for trusted, stylish baby gear designed to simplify life for modern parents.

Uppababy Minu Duo Lightweight Stroller Features:

From-Birth Ready: Two full-size, spacious seats with integrated foot barriers accommodate children from day one up to 50 lbs each.
Compatible with Car Seats: Attach the Uppababy Aria or Mesa infant car seat using adapters (sold separately) for a smooth transition from vehicle to stroller.
One-Hand, One-Step Fold: Quickly folds in one fluid motion and locks in place, making transport and storage simple and convenient.
Narrow Frame: Slim 27.8” design fits through standard doorways and navigates tight spaces effortlessly—Disney-park approved!
Integrated Carry Handle: Lift the folded stroller easily using the built-in crossbar carry handle for on-the-go convenience.
Customizable Seating: Both seats can be independently adjusted to accommodate different-sized children, perfect for twins or a toddler and newborn.
Comfort and Safety: No-rethread 5-point harnesses offer secure, growing fit—narrow enough for infants, expandable for toddlers.
Integrated Footrests: Two-position footrests and foot barriers provide added comfort and leg support at every stage.
Built-In AirTag Pocket: Hidden pocket lets you add your own Air Tracker (sold separately) to keep tabs on your stroller in crowded areas like parks or airports.
Lightweight Durability: True weight of 27.4 lbs offers robust structure without the bulk, ideal for travel and everyday use.
Unfolded Dimensions: 37.2? L x 27.8? W x 40.5? H
Folded Dimensions: 12.9? L x 27.8? W x 23.7? H
Weight: 27.4 lbs
Harness: 5-point, no-rethread
Footrests: Two-position adjustable
AirTag Compatibility: Hidden pocket for Air Tracker
Car Seat Compatibility: Uppababy’s Aria and Mesa infant car seats (with adapters sold separately)
Recommended Use: Birth to 50 lbs per seat

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SKU: 36815433969

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4.9 ★★★★★
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Zygerian99
Fort Morgan, US
★★★★★ 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
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Verified Purchase
Shannon
Phoenix, US
★★★★★ 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
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William P Ross
Carnegie, 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
Waukegan, 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
New York, 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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