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Description
silla uppababy cruz ᐅ Cochecito Uppababy Cruz V3 Silla y CapazoEl UPPAbaby Cruz V3 Do ofrece la combinacin perfecta de funcionalidad, diseo y confort desde el nacimiento. Este conjunto incluye el cochecito Cruz V3 y el capazo V3, creando un sistema completo que acompaa a tu beb desde sus primeros das hasta los 22 kg. Compacto pero sin renunciar a nada, el Cruz V3 destaca por su manejo gil, su plegado intuitivo y su calidad premium en cada detalle. Con el capazo incluido, podrs disfrutar de paseos seguros y cmodos
El UPPAbaby Cruz® V3 Dúo ofrece la combinación perfecta de funcionalidad, diseño y confort desde el nacimiento. Este conjunto incluye el cochecito Cruz V3 y el capazo V3, creando un sistema completo que acompaña a tu bebé desde sus primeros días hasta los 22 kg.
Compacto pero sin renunciar a nada, el Cruz® V3 destaca por su manejo ágil, su plegado intuitivo y su calidad premium en cada detalle. Con el capazo incluido, podrás disfrutar de paseos seguros y cómodos desde el nacimiento, mientras que la silla ofrece una conducción suave y estable para niños mayores.
Diseñado con suspensión avanzada, tejidos de alto rendimiento y materiales duraderos con certificación REACH, el Cruz® V3 Dúo combina elegancia, practicidad y versatilidad para adaptarse a la vida urbana y familiar.
Características destacadas
Desde el nacimiento
- Incluye capazo V3: Ideal para los primeros meses, con colchón de malla transpirable y base ventilada para un descanso seguro y cómodo.
- Modo capazo y modo silla: Transforma el cochecito en un sistema de paseo completo apto desde el nacimiento hasta los 22 kg.
- Reductor para recién nacidos: Integrado en la hamaca para una posición ergonómica y segura desde el primer día.
Paseos suaves y confortables
- Suspensión FlexRide™ mejorada: Suaviza cada movimiento para un paseo estable en todo tipo de superficies.
- Neumáticos antipinchazos: Con opción de bloqueo delantero para mayor control y estabilidad.
- Reposapiernas ajustable y reclinación total: Cuatro posiciones que garantizan comodidad en cada etapa.
Diseño práctico y funcional
- Plegado compacto: Gatillos de doble acción para un cierre rápido y sencillo, incluso con el asiento colocado.
- Cesta extragrande de fácil acceso: Capacidad hasta 13,6 kg, ideal para compras y accesorios.
- Asiento reversible: Orientado hacia los padres o hacia el mundo, con reclinación completa.
- Manillar ajustable: En altura, con acabados en flor de piel 100 % natural.
Protección y seguridad
- Capota extensible con UPF 50+: Impermeable, con paneles de ventilación y parasol extensible con cremallera.
- Hebilla magnética y arnés autoajustable: Sistema rápido y seguro para abrochar al bebé sin esfuerzo.
- Embellecedores reflectantes: Mayor visibilidad en paseos con poca luz.
Detalles premium
- Telas de alta gama: Transpirables, duraderas y con tratamiento resistente al agua.
- Materiales con certificación REACH: Libres de sustancias nocivas.
- Diseño elegante y ergonómico: En color Jake (Charcoal | Carbon Frame | Black Leather), con acabados de lujo.
- Garantía extendida de 3 años.
Capazo V3
Comodidad y transpirabilidad
- Colchón de malla de aire sin espuma: Lavable y con el doble de flujo de aire que un colchón tradicional, para una transpirabilidad óptima.
- Base ventilada: Favorece la circulación del aire y mantiene un entorno saludable para el bebé.
- Ideal para dormir toda la noche: Aprobado como superficie segura de descanso al combinarlo con el soporte para capazo (se vende por separado).
Protección en todas las estaciones
- Capota extensible con UPF 50+: Protege de los rayos UV y del viento.
- Ventilación cruzada con cremallera: Ajustable según la temperatura.
- Mosquitera incluida: Protección adicional frente a insectos.
- Cubrepiés con doble cremallera: Permite acceder al bebé fácilmente.
Practicidad
- Asa de transporte integrada: Permite desmontar el capazo con una sola mano.
- Compatible con Cruz®, Vista®, Ridge® y Minu®.
Especificaciones
Cochecito CruzV3
- Uso: Desde el nacimiento hasta 22 kg (altura máxima del niño 101,6 cm).
- Peso del conjunto:
Chasis + asiento: 11,5 kg.
Chasis: 8,6 kg.
Asiento: 2,9 kg. - Dimensiones:
Abierto: 95,2 L × 57,9 A × 101,6 Al cm.
Plegado con asiento: 43,6 L × 56,8 A × 82 Al cm.
Plegado sin asiento: 26,9 L × 56,8 A × 106,9 Al cm. - Cesta portaobjetos: hasta 13,6 kg.
- Garantía: 3 años.
Capazo V3
- Uso: Desde el nacimiento hasta los 9 kg (aprox. 6 meses).
- Peso máximo recomendado: 9 kg / 20 lb.
- Materiales:
- Estructura: aluminio y textil resistente.
- Colchón: malla 3D transpirable.
- Funda lavable.
- Color: Greyson (Charcoal Mélange | Carbon Frame).
Qué incluye
- Cochecito Cruz® V3 (chasis + hamaca).
- Reductor para recién nacidos.
- Mosquitera y burbuja de lluvia para el asiento.
- Capazo V3 con colchón y cubrepiés.
- Garantía extendida de 3 años.
Compatibilidad
- Compatible con capazo V3 (incluido).
- Compatible con la silla de auto Mesa® i-Size sin adaptadores.
- Admite el accesorio PiggyBack para transportar a un segundo niño.
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Exchange/Return Notes
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4.1 ★★★★★
Based on 1685 reviews
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Product Reviews
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it.
For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would.
The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is.
This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 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