SKU: 37559799295
uppababy vista v2 pink

uppababy vista v2 pink UPPAbaby Vista V2 Stroller, Gwen

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Description

uppababy vista v2 pink UPPAbaby Vista V2 Stroller, GwenFeatures of UPPAbaby Vista V2 Double Stroller The UPPAbaby Vista V2 Double Stroller is a favorite for a reason. Quick glance features: UPPAbabys bestselling stroller grows with your family! The UPPAbaby Vista V2 double stroller is the perfect choice for growing families and now you can get everything you need all in one convenient set. Whether youre expecting a second child, twins, or even a third child, the Vista V2 can configure to allow everyone to

Features of UPPAbaby Vista V2 Double Stroller

The UPPAbaby Vista V2 Double Stroller is a favorite for a reason. Quick glance features:

UPPAbaby’s bestselling stroller grows with your family!

The UPPAbaby Vista V2 double stroller is the perfect choice for growing families and now you can get everything you need all in one convenient set. Whether you’re expecting a second child, twins, or even a third child, the Vista V2 can configure to allow everyone to stroll in comfort.

Features of the UPPAbaby Vista V2 Double Stroller Set

The UPPAbaby Vista V2 Double Stroller Set comes with everything you need for an infant, but if you have an older child or are expecting twins, buying this convenient set adds an extra Rumbleseat, which makes strolling as a family a breeze. Features of the UPPAbaby Vista V2 Double Stroller Set include:

  • Choose from multiple configurations
  • Aluminum alloy stroller frame
  • Easy to maneuver
  • Machine washable seat fabrics, lay flat to dry
  • Includes:

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Additional, but optional Vista V2 configuration options we recommend

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How to use the UPPAbaby Vista V2 Double Stroller with two car seats

The UPPAbaby Vista V2 double stroller is compatible with a number of car seats that can be mounted together. These are the options:

  • UPPAbaby Mesa
    To use the Vista V2 with two Mesa car seats, you need the Vista Upper Adapters and Vista Lower Adapters. 
  • Nuna Pipa / Maxi Cosi / Cybex / Clek
    To use the Vista V2 as a double with two Nuna Pipa Infant Car Seats, Maxi Cosi Infant Car Seats, Cybex Car Seats, or Clek Car Seats you'll need UPPAbaby's Maxi Cosi / Nuna / Cybex Upper Adapter and UPPAbaby's Maxi Cosi / Nuna / Cybex Lower Adapter.

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UPPABABY VISTA V2 DOUBLE STROLLER SPECIFICATIONS

Type Of Stroller:

Premium single/double

Maximum Carrying Load:

50 lbs as a single
85 lbs with Rumbleseat

Newborn Suitable:

Yes, with included bassinet or optional SnugSeat

Infant Seat Compatible:

Yes, compatible with Uppababy Mesa (no adapter required), and certain Maxi Cosi, Nuna, Cybex, Clek and Chicco infant car seats (adapters required)

Frame Material:

Aluminum

Stroller Weight:

26.6 lbs with Toddler Seat attached
32.6 lbs with Rumbleseat added

Open Length:

46"

Open Width:

25.7"

Open Height (to top of handle):

42.5" with handle fully extended

Tire:

8" front polyurethane, 11" rear AirGo no-flat

Folded Size:

33" x 25.7" x 19" (one seat attached with bumper bar)

Brakes:

One foot pedal controls both rear wheels

Warranty:

2 Year (3 years with Ub XTEND)

 

FAQs about the UPPAbaby Vista V2 Double Stroller

Do you need the upper and lower bracket adapters for the Uppababy Vista V2 double stroller?
Yes, however please keep in mind that the purchase of a Rumbleseat includes lower adapters. If you are converting to a double with a Rumbleseat, you will need to purchase the Rumbleseat and upper adapters.  For all other configurations, you will need to purchase both the upper adapters and lower adapters.

Can newborn infants use the Vista V2 stroller seat?
The Vista V2's stroller seats are recommended for 3-months and up. For newborns, you can add the Snugseat Infant Insert, use the Bassinet, or use an UPPAbaby Car Seat Adapter.

What is the weight limit for the UPPAbaby Vista V2 as a double?
The weight capacity for the main upper seat is 50 pounds, and the weight limit for the Rumbleseat is 35 pounds.

Can you fold the UPPAbaby Vista V2 double stroller with both seats attached?
It is possible to fold the UPPAbaby Vista V2 with both seats attached. However, the folded size will be bulky and difficult for most moms to lift into a car. It's more practical, if folding with both seats, for stowing in a closet or hallway.

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SKU: 37559799295

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Natrona Heights, US
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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.
Fort Morgan, US
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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
Massapequa, 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
Grantham, 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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Verified Purchase
Amazon Customer
Omaha, US
★★★★★ 4
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Nice learning book just have to finish it
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