SKU: 32362578185
stroller carrier car seat combo

stroller carrier car seat combo Doona Infant Car Seat Stroller Combo With Latch Base

$23.75 $26.39
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Description

stroller carrier car seat combo Doona Infant Car Seat Stroller Combo With Latch BaseDoona Infant Car Seat Stroller with Latch Base is the world's first fully integrated car seat and stroller combo, converting from car seat to stroller in one seamless motion without ever disturbing your baby. For parents searching for the best car seat stroller combo for travel, city living, and everyday convenience, the Doona eliminates the need to pack a separate stroller entirely, saving trunk space, saving money, and saving the moment when your

Doona Infant Car Seat Stroller with Latch Base is the world's first fully integrated car seat and stroller combo, converting from car seat to stroller in one seamless motion without ever disturbing your baby. For parents searching for the best car seat stroller combo for travel, city living, and everyday convenience, the Doona eliminates the need to pack a separate stroller entirely, saving trunk space, saving money, and saving the moment when your baby finally falls asleep on the way home.

At 17.2 lbs, the Doona is built from premium fiber-reinforced polymers and rust-free aluminum and has been crash-tested over 100 times to meet the strictest US and EU safety standards for car seats, strollers, and hand-held carriers simultaneously. The adjustable handlebar doubles as an anti-rebound bar inside the vehicle, and the double-wall construction provides side impact protection while housing the integrated wheel system. Fail-safe mechanisms throughout the design reduce the risk of misuse, which accounts for the majority of car seat injuries. Compare the Doona to the Nuna TRVL lx + PIPA Urbn Travel System: both are premium travel solutions, but Doona is a true 2-in-1 (car seat becomes stroller instantly without transfer), while Nuna TRVL requires a separate stroller frame and involves moving your baby. Choose Doona for instant conversion and maximum simplicity; choose Nuna TRVL if you want modularity and stroller frame flexibility. The Doona is TUV and FAA approved for aircraft use, making it the ideal travel companion for flying families. Available in multiple colors and suitable from 4 to 30 lbs, the Doona comes complete with the LATCH base, infant insert, head support, and vehicle seat protector included, making it one of the most complete and convenient infant car seat packages available.

Doona Car Seat Stroller Features

  • Car seat to stroller conversion in one motion without disturbing baby—no transfer required
  • Three modes: car seat, stroller, and pull-along carrier for crowded spaces
  • Rear-facing only for infants 4 to 30 lbs and up to 32 inches
  • 5-point harness for secure restraint in both car seat and stroller mode
  • Adjustable handlebar that doubles as an anti-rebound bar in the vehicle, reducing whiplash forces
  • Double-wall side impact protection with energy-absorbing construction
  • Fail-safe mechanisms engineered to reduce misuse and improper installation
  • Crash-tested over 100 times to meet US and EU standards for car seats, strollers, and carriers
  • Newborn ergonomic support developed with global engineering, safety, and medical experts
  • UPF 50+ water-repellent canopy for all-weather sun and weather protection
  • Baby-safe materials free of harsh chemicals and tested to strict European standards
  • Removable and washable textiles for easy maintenance
  • TUV and FAA approved for aircraft travel and TSA pre-approved when used as car seat
  • 2-year manufacturer's warranty
  • Lightest 2-in-1 car seat stroller combo at 17.2 lbs—comparable weight to infant car seats alone

Doona LATCH Base Features

  • Rear-facing only installation via LATCH system or vehicle seat belt
  • Easy-to-read level indicators for correct seat angle confirmation
  • Green lock indicator confirms Doona is safely secured in base
  • Compliant with FMVSS 213 US safety standards
  • What's included: LATCH base, infant insert, head support, and vehicle seat protector

See Entire Doona Collection

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SKU: 32362578185

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Hashi Hanta
Port Orchard, US
★★★★★ 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
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Carol
Battle Creek, US
★★★★★ 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
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Grantham, US
★★★★★ 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
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Par
Phoenix, US
★★★★★ 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
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Richard Hackathorn
Cuba, 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

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