SKU: 39624186181
yoyo double stroller

yoyo double stroller YOYO Connect Frame

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Description

yoyo double stroller YOYO Connect FrameBabyzen YOYO Connect Frame converts your existing YOYO or Stokke YOYO into a double stroller with a single click, attaching to the back of the original frame without tools or adapters. For families with twins or two children close in age, it eliminates the need to buy an entirely new stroller. The combined system offers five configurations covering bassinet and bassinet, bassinet and 6+ seat, two 6+ seats, and car seat combinations, which means the

Babyzen YOYO Connect Frame converts your existing YOYO² or Stokke YOYO³ into a double stroller with a single click, attaching to the back of the original frame without tools or adapters. For families with twins or two children close in age, it eliminates the need to buy an entirely new stroller. The combined system offers five configurations covering bassinet and bassinet, bassinet and 6+ seat, two 6+ seats, and car seat combinations, which means the setup grows alongside your children from newborn through toddler years. The frame uses the same patented folding system as the YOYO³ so the fold sequence is identical to what you already know — one motion, same compact result, still accepted as cabin luggage on most airlines.

The frame measures 17.3 inches wide which is narrow enough to clear standard doorways and move through tight urban spaces without turning sideways. Each seat position holds up to 48.5 lbs and the reversible backrest adjusts independently for each child. The 4-wheel braking system locks both frames simultaneously with one step. At 13 lbs the Connect adds meaningful capacity without making the combined system unwieldy. The shopping basket is included and accessible while both seats are in use. ANB Baby carries the full Babyzen collection and all orders over $45 ship free.

Features

  • Converts YOYO² or Stokke YOYO³ into a double stroller in one click
  • 5 seating configurations for twins or siblings
  • Same patented folding system as YOYO³
  • 17.3 inches wide — fits through standard doorways
  • Accepted as cabin luggage on most airlines
  • 4-wheel simultaneous braking system
  • Reversible backrest on second seat
  • 48.5 lbs weight capacity per seat
  • Suitable from birth to 4 years when used with compatible accessories
  • Available in White and Peppermint frame colors
  • 2-year warranty

See Entire Babyzen Collection

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SKU: 39624186181

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4.2 ★★★★★
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Richard Hackathorn
Boise, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Fort Morgan, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Alexandria, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Charlottesville, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Bozeman, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022

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