SKU: 69278817047
cybex portugal

cybex portugal Cybex Libelle 2026 — Carrinho Ultraleve para Viagens de Avião

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Description

cybex portugal Cybex Libelle 2026 — Carrinho Ultraleve para Viagens de AviãoUltraleve, ultracompacto e pronto para qualquer aventura. O Cybex Libelle (2026) foi desenhado para pais ativos que no abdicam de conforto e praticidade seja numa viagem de avio, num passeio urbano ou numa escapadinha de fim de semana. Principais vantagens Adequado a partir dos 6 meses at aproximadamente 4 anos (22 kg) Compatvel com bagagem de mo na maioria das companhias areas Dobra ultracompacta numa s mo, em segundos Reclinao confortvel para sestas

Ultraleve, ultracompacto e pronto para qualquer aventura. O Cybex Libelle (2026) foi desenhado para pais ativos que não abdicam de conforto e praticidade — seja numa viagem de avião, num passeio urbano ou numa escapadinha de fim de semana.

Principais vantagens

  • Adequado a partir dos 6 meses até aproximadamente 4 anos (22 kg)
  • Compatível com bagagem de mão na maioria das companhias aéreas
  • Dobra ultracompacta numa só mão, em segundos
  • Reclinação confortável para sestas em movimento
  • Capota XL com proteção UV
  • Compatível com cadeiras auto Cybex (com adaptadores Libelle/Orfeo)
  • Compatível com Cot S / Cot S Lux para uso desde o nascimento
  • Disponível em 8 combinações de cor e armação

DESCRIÇÃO COMPLETA

"Cabe no avião?"

Sim! O Libelle foi especificamente desenhado para caber como bagagem de mão na maioria das companhias aéreas — incluindo Ryanair, EasyJet e TAP. Não precisas de despachar o carrinho nem de pagar taxas extra. Dobra em segundos com uma só mão e cabe no porta-bagagens de bordo.

"É confortável para o meu bebé ou é demasiado básico?"

É uma dúvida legítima. Os carrinhos de viagem têm fama de ser desconfortáveis — mas o Libelle é diferente. O assento reclinável permite sestas confortáveis durante o passeio, e a capota XL com proteção UV garante sombra real em dias de sol. O teu bebé vai estar bem, mesmo em viagens longas.

"Posso usá-lo desde o nascimento?"

Com os adaptadores adequados (vendidos separadamente), o Libelle é compatível com as cadeiras auto Cybex e com as alcofas Cot S e Cot S Lux — tornando-o utilizável desde o nascimento como parte de um travel system completo. Se já tens outros produtos Cybex, o Libelle encaixa perfeitamente no teu sistema.

"Aguenta o uso diário ou é só para viagens?"

O Libelle é leve o suficiente para levares sempre contigo, mas robusto o suficiente para o dia a dia. Muitas famílias usam-no como carrinho principal em contexto urbano — metro, autocarros, centros comerciais — precisamente pela facilidade de dobrar e transportar. Para terrenos mais irregulares ou passeios longos no campo, o Balios S Lux pode ser uma opção mais indicada.

"Qual a diferença para o Balios S Lux?"

O Libelle é mais leve e compacto — ideal para viagens e mobilidade urbana. O Balios S Lux tem suspensão nas 4 rodas, cesto maior e é mais indicado para uso intensivo em qualquer terreno. Se viajam muito de avião ou precisam de um segundo carrinho prático, o Libelle é a escolha certa. Se querem um único carrinho para tudo, o Balios S Lux pode ser mais completo.

Design moderno em 8 combinações

Disponível em 8 combinações de cor e armação — preta ou taupe — com capota em tons como Cinnamon Yellow, Dune Grey, Candy Pink, Magic Black, Chocolate Brown, Moss Green, Stormy Blue ou Almond Beige. Escolhe a combinação que melhor se adapta ao teu estilo.

Detalhes técnicos

  • Idade recomendada: 6 meses até 22 kg (~4 anos)
  • Dobra compacta com uma mão
  • Capota XL com proteção UV
  • Reclinação do assento ajustável
  • Compatível com bagagem de mão (maioria das companhias aéreas)
  • Compatível com cadeiras auto Cybex (adaptadores Libelle/Orfeo, vendidos separadamente)
  • Compatível com Cot S / Cot S Lux (vendidos separadamente)
  • Saco de transporte disponível em separado

Porquê comprar na Minicool:

  • Somos revendedor oficial Cybex
  • Assistência personalizada antes e após a compra
  • Entrega rápida e segura
  • Loja física para ver e experimentar o carrinho
  • Ajudamos a escolher o melhor carrinho para o estilo de vida da tua família

Ainda tens dúvidas?

Diz-nos a companhia aérea que usas habitualmente, a idade do bebé e se já tens outros produtos Cybex — ajudamos a confirmar se o Libelle é a melhor opção para ti. Podes falar connosco por chat, telefone ou visitar-nos na loja.

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SKU: 69278817047

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4.7 ★★★★★
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William P Ross
Natrona Heights, 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
Lake Worth, 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
Dallas, 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
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mackster
Chelsea, 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
Carnegie, 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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