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Description
bugaboo 2015 donkey twin stroller Bugaboo Donkey 5 Mono Complete StrollerThe Bugaboo Donkey 5 Twin Bassinet & Seat Complete Stroller is the best side by side double stroller for twins. The Donkey 5 Twin lets your children discover the world together from day one. Bugaboo Donkey 5 Twin Complete Features Side by side design for sibling connection Fits through standard doorways One hand maneuver on all terrains Underseat basket holds 22 lbs Breezy bassinet with aerated mattress Large extendable UPF 50+ sun canopy
The Bugaboo Donkey 5 Twin Bassinet & Seat Complete Stroller is the best side-by-side double stroller for twins. The Donkey 5 Twin lets your children discover the world together from day one.
Bugaboo Donkey 5 Twin Complete Features
- Side-by-side design for sibling connection
- Fits through standard doorways
- One-hand maneuver on all terrains
- Underseat basket holds 22 lbs
- Breezy bassinet with aerated mattress
- Large extendable UPF 50+ sun canopy
- Independently reversible and reclinable seats
- Adjustable five-point safety harness
- One-piece self-standing fold or two-piece compact fold
- Car seat compatible (adapters needed)
Side-by-side design - Let your children roam the world side by side, at the same height. The seats can be arranged to face you, face each other or face the path ahead.
Slim configurations - At 74 cm/29.1 inches, the Donkey 5 Twin fits through standard doorways and elevators. In single mode, it’s as wide as a Fox 5, at 60 cm/23.6 inches wide.
Easy to maneuver - Steer the Donkey 5 Twin with just one hand and travel on all terrains, even when you're strolling with two children and groceries.
Elevated seat and bassinet mean your children are always together & never out of reach
Breezy bassinet & aerated mattress keep your baby comfy no matter the weather
Large puncture proof wheels and easy to maneuver on all terrains
Integrated wheeled board and cupholder attachment points for your favorite accessories
Improved self-standing one piece fold makes storing the stroller easier than ever
- A Sneak Peak of The World: The breezy bassinet keeps your baby cool & comfy while giving them a glimpse of what’s going on outside. When it gets windy, seal the panel shut for some extra warmth.
- Softer Bed For Longer Naps: The aerated mattress lets your newborn sleep and breathe easily even when lying flat on their tummy.
- Bump-free Adventures: Navigating a crowded sidewalk or exploring the great outdoors is super smooth, thanks to the large puncture proof wheels.
- Smaller Than You Think - At 29 in, the Donkey 5 Duo fits through most standard doorways. Switching from Mono to Duo only adds 5.5 in, meaning you can confidently navigate hallways or supermarkets with your whole family in tow.
- Together Wherever - Let your children roam the world side by side from day one in Twin mode. The seats can be arranged to face you, face each other or face the path ahead. The higher seat & bassinet positions also allow you to watch your kids bond right in front of your eyes.
- Grab Fold & Go: Make your double stroller compact in seconds with the self-standing one piece fold design. Unfolding is just as quick and easy—the advanced friction lock lets you get it up and running without pushing any buttons at all.
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4.4 ★★★★★
Based on 17 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
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