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Description
used bugaboo donkey mono Bugaboo Donkey 6 StrollerBugaboo Donkey 6 Complete Stroller Single to Double Side by Side Stroller The Bugaboo Donkey 6 Complete Stroller is a premium single to double stroller designed for growing families. Its patented expandable frame converts from a single stroller to a side by side double in just three clicks, making it ideal for siblings or twins. With large puncture proof all terrain wheels, one hand maneuverability, and a slim 29. 1 inch width in double mode, it fits
Bugaboo Donkey 6 Complete Stroller – Single to Double Side-by-Side Stroller
The Bugaboo Donkey 6 Complete Stroller is a premium single-to-double stroller designed for growing families. Its patented expandable frame converts from a single stroller to a side-by-side double in just three clicks, making it ideal for siblings or twins.
With large puncture-proof all-terrain wheels, one-hand maneuverability, and a slim 29.1-inch width in double mode, it fits through standard doorways while delivering smooth performance on city streets and uneven terrain.
The updated all-season bassinet features 2x larger breezy panels and soft 100% organic cotton lining for newborn comfort. The stroller includes a 50% larger underseat basket with a 33 lb capacity, plus a redesigned side luggage bag that holds up to 8.8 lbs.
Crafted from 100% recycled PET fabrics, 60% recycled aluminum, and bio-based plastics, the Donkey 6 reduces environmental impact while maintaining premium durability and color longevity.
Why Parents Love the Bugaboo Donkey 6
✔ Converts from Single to Double in 3 Clicks
The patented expansion system lets you go from Mono (single) to Duo (double) mode in seconds.
- Accommodates one or two children side by side ( sibling seat sold separately )
- Add a wheeled board for a third rider ( sold separately )
- Over 20+ configurations with bassinets, seats, and car seats Perfect for parents planning ahead or navigating life with siblings or twins.
✔ Side-by-Side Comfort From Day One
- Spacious all-season bassinet(s) with 2x larger breezy panels for improved airflow ( sibling seat / 2nd bassinet sold separately )
- Soft 100% organic cotton lining — naturally white and gentle on baby’s skin
- Reversible, reclinable toddler seats for growing children
- Both children ride at the same height for bonding and reassurance ( sibling seat / 2nd bassinet sold separately )
- Whether you’re strolling a newborn and toddler or twins, the Donkey 6 keeps them connected and comfortable.
✔ Smooth, One-Handed Maneuverability on Any Terrain
The Donkey 6 glides effortlessly thanks to:
- XL puncture-proof all-terrain wheels
- Tight turning radius for navigating curbs and corners
- Easy one-hand steering — even fully loaded
- Only 29.1 inches wide in double mode (fits standard doorways)
- From tight city spaces to outdoor adventures, this stroller handles it all.
✔ 50% More Storage Space
- Underseat basket holds up to 33 lbs
- 50% more storage than previous model
- Redesigned side bag holds up to 8.8 lbs and attaches securely to the handlebar
- Side bag doubles as a convenient changing bag
- Perfect for errands, travel, and full-day outings.
Sustainably Designed for the Future
The Bugaboo Donkey 6 is thoughtfully engineered with lower environmental impact:
- 100% recycled PET bottle fabrics
- 60% recycled aluminum frame tubes
- Bio-based plastics from renewable sources
- 17% reduced carbon footprint compared to previous generation
- Water- and energy-efficient dope-dyed fabrics (select colors)
- The result? Richer, more fade-resistant colors and long-lasting durability — with less water and fewer chemicals used in production.
Bugaboo Donkey 6 Key Features
- Converts from single to double stroller
- Side-by-side seating at equal height
- Suitable from birth (bassinet included)
- Reversible, reclinable toddler seats
- 29.1” width in double mode
- 33 lb underseat basket capacity
- 8.8 lb side luggage basket
- XL all-terrain wheels
- Sustainable recycled materials
- Over 20 configurations with car seats & wheeled board
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4.2 ★★★★★
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Product Reviews
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people.
THANK YOU Kent for writing this book.
We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
★★★★★ 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
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