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Description
bugaboo bee 5 vs bee 6 Bugaboo Bee 6 Complete Stroller Mineral CollectionBugaboo Bee 6 is the new generation urban stroller, suitable from birth 50 lbs. Outstanding among others, Bee 6 offers a rare combination of travel size dimensions and full size stroller comfort. The beautiful Mineral Collection features refined fabrics inspired by the colors of nature, with matching seat fabrics and an under seat basket. The leather look one hand steering bar, and 4 wheel suspension tires, create smooth navigation regardless of
Bugaboo Bee 6 is the new generation urban stroller, suitable from birth - 50 lbs. Outstanding among others, Bee 6 offers a rare combination of travel size dimensions and full-size stroller comfort. The beautiful Mineral Collection features refined fabrics inspired by the colors of nature, with matching seat fabrics and an under-seat basket. The leather-look one-hand steering bar, and 4 wheel suspension tires, create smooth navigation regardless of terrain. The seat of this stroller offers multiple recline levels, including an almost complete lie-flat mode in both parent and street-facing positions. A rotating bumper bar contributes extra child support while maintaining easy accessibility. High-performance mesh seat fabric with integrated holes in the seat hardware, along with an extendable sun canopy featuring a double flap peekaboo window, enables maximum ventilation during hot summer days. This stroller contours to parent and baby with optional adjustment to the seat length, harness straps, and push bar height. In addition, this stroller is lightweight to carry, offers compact one-hand fold and unfold, free-standing capabilities, and car seat compatible with included adapters, all comprising to a preferred travel companion.
Specifications
- Lightweight, compact and comfortable.
- Refined look with matching under-seat basket and fabrics
- Large 7" tires for smooth navigation on bumpy surfaces.
- 4 puncture-proof swivel wheels with full suspension.
- Custom size adjustable seat length, harness strap, and push bar.
- Canvas canopy with double peekaboo flaps for cool summer strolls.
- Breezy high-performance seat fabric.
- Integrated holes in the seat hardware for maximum ventilation.
- Compact freestanding fold.
- Compatible with Bugaboo TurtleOne, and Maxi Cosi car seat with included adapters. (Car seats sold separately)
- Compatible with the Bugaboo Bee 6 Bassinet. (sold separately)
- One-hand fold and unfold.
- Rotating bumper bar offering extended child security and easy access.
- 5 point harness.
- Leather-look grips.
- 100% Polyester canopy and seat fabric.
- Machine wash cold canopy and seat fabric.
- 2+2 years limited warranty registration required.
Dimensions & Weight
- Folded 30.98" x 18.58" x 15.47"
- Unfolded 30.98" x 20.8" x 15.47
- 23.1 lbs.
- Suitable from birth- 50 lbs.
- Basket weight capacity 8.8 lbs.
What's Included
- Complete Stroller frame
- Grips
- Rotating bumper bar
- 5 point harness straps
- Canopy
- Self stand extension
- Rain cover
- Car seat adapters
|
1.5 Inch Taller Bassinet Position |
| Larger 7" Wheels Offers Lighter Push |
|
Breezy Peekaboo Panels |
| Free Standing Compact Fold |
|
Aerated Mesh Bassinet Panel |
| Easy Slide Security Bumper Bar |
|
High Performance Breathable Seat Fabric |
| Enhanced Black Hardware Bolts |
|
Integrated Ventilation Holes |
| Included Car Seat Adapters |
|
Improved Handlebar Grip Protection |
| Increased Recline |
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
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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.
- To process your return/exchange, please contact us at [email protected]
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4.2 ★★★★★
Based on 25 reviews
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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.
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
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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