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Description
stokke orange Stokke YOYO3 Ultra Compact 6+ Stroller (Riviera Limited Edition)YOYO is the most versatile stroller that grows with your child, from birth to toddler. Sturdy and super maneuverable, it has been designed to provide optimal daily comfort to your child. YOYO stroller from 6 months has double the agility and ingenuity with its signature folding and unfolding. It can be stored in the smallest space at home, takes up very little room in the trunk of a car and can be worn over the shoulder. Catch the subway, get on a
YOYO³ is the most versatile stroller that grows with your child, from birth to toddler. Sturdy and super maneuverable, it has been designed to provide optimal daily comfort to your child.
YOYO³ stroller from 6 months has double the agility and ingenuity with its signature folding and unfolding. It can be stored in the smallest space at home, takes up very little room in the trunk of a car and can be worn over the shoulder. Catch the subway, get on a bus, jump in a taxi or fly to the other side of the world!
With a range of trendy colors and accessories to choose from, you can make it your own and find the perfect fit for your needs and style.
With YOYO³ you'll be ready for everything.
- Folds in and out in a flash
- Compact, can be stored anywhere
- Suitable for air travel, folded cabin baggage dimensions*
- Ultra-lightweight, easily carried over the shoulder
- Extendable anti-UV canopy with peak-a-boo and closeable ventilation window
- Equipped with a 5-point harness
- Redesigned seat pad: new pillow and increased backrest height with mesh strap protectors
- Sturdy and super maneuverable with reflecting wheels for added safety
- Increased child comfort: 4 wheel suspension (Hytrel® elastomer technology) and ""Soft Drive"" system
- Extra storage: under seat basket with 10kg/22lbs capacity and zipped back pocket to keep essentials close at hand
- Foldable handlebar in soft faux leather with tether strap
- Multi-position reclining backrest
- Anti-UV (UPF 50+) water-repellent fabric
* Cabin luggage standards may vary depending on the airline, we recommend checking with your carrier for the latest applicable cabin luggage allowance.
Specifications
- Suitable for age: from 6 to 48 (months)
- Suitable for weight: 22 kg / 48.5 lbs
- Extended warranty: 2 (years)
- Complete stroller weight (seat and chassis): 6.7 kg / 14.8 lbs
- Chassis weight: 6.3 kg / 13.9 lbs
- Seat weight: 0.4 kg / 0.9 lbs
- Chassis with seat dimension: 106 x 86 x 44cm (41.7 x 33.8 x 17.3 in)
- Folded chassis with seat dimension: 52 × 44 × 18 cm (20.5 × 17.3 × 7.1 in)
What's In The Box
- 1 protective bag
- 1 stroller frame
- 1 6+ seat base
- 1 reversible backrest
- 1 canopy
- 1 seat pad
- 1 shopping basket
- 2 6+ canopy wires
- 1 carrying strap
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
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- Standard Shipping : 3-10 business days
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
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4.3 ★★★★★
Based on 28 reviews
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Product Reviews
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 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
★★★★★ 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
★★★★★ 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