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Description
uppababy vista anthony UPPAbaby Vista V2 + Mesa V2 Travel System, Anthony/Jake (White & Grey – CapitalkidzVista V2 Stroller and Mesa V2 Infant Car Seat Bundle This all in one travel system offers unmatched convenience, safety, and style for parents on the go, combining the Vista V2 stroller and Mesa V2 car seat for a seamless transition from car to stroller. Vista V2 Stroller & Bassinet Highlights: Age Range & Weight: Suitable from 3 months to 50 lbs with a full size, reversible toddler seat. Comfort & Sun Protection: Higher backrest, longer leg rest, and
Vista V2 Stroller and Mesa V2 Infant Car Seat Bundle
This all-in-one travel system offers unmatched convenience, safety, and style for parents on the go, combining the Vista V2 stroller and Mesa V2 car seat for a seamless transition from car to stroller.
Vista V2 Stroller & Bassinet Highlights:
- Age Range & Weight: Suitable from 3 months to 50 lbs with a full-size, reversible toddler seat.
- Comfort & Sun Protection: Higher backrest, longer leg rest, and deeper footrest for a cozy ride. The extended canopy provides UPF 50+ protection with mesh panels for ventilation.
- Smooth Ride: Enhanced all-wheel suspension and softer tires create a cushioned experience, while front-wheel locks add stability.
- Easy Storage & Handling: The large basket offers ample storage with additional pockets. The stroller folds in one step and stands upright when folded, making storage simple.
- Premium Finish: Features 100% full-grain leather accents for a stylish touch.
- Weather Protection: Comes with both rain and bug shields.
Stroller Specs:
- Open Dimensions: 36” L x 25.7” W x 39.5” H
- Folded Dimensions: 17.3” L x 25.7” W x 33” H
- Weight: 27 lbs
What’s in the Box: Frame, wheels, seat, bassinet, bumper bar, storage bag, rain cover, and bug cover.
Mesa V2 Car Seat & Base Highlights:
The Mesa V2 car seat is engineered for safety, comfort, and convenience, featuring advanced installation, side-impact protection, and a no-rethread harness.
-
Safety Features:
- 4th Anti-Rebound Handle Position: Increases safety by minimizing movement in rebound and rear-impact collisions.
- Adjustable Headrest with Enhanced Side Impact Protection (SIP): A large headrest provides improved containment and additional protection.
- European Routing: Offers a secure installation option when using the seat without the base.
-
Newborn Fit & Comfort:
- Infant Insert & Low Harness Position: Optimized for babies approximately 4-11 lbs.
- No-Rethread Harness: Easily adjusts with the headrest for a perfect fit as your baby grows.
- Harness Storage Pockets: Conveniently hold buckles out of the way when placing the child in the seat.
- One Crotch Buckle Position: Provides a comfortable, secure fit.
- Removable & Washable Seat Fabric: Easy to clean and free from flame-retardant chemicals.
-
Convenience & Compatibility:
- Lightweight Carrier: Weighs under 10 lbs, making it easy to lift and carry.
- Large, Hideaway Canopy: UPF 25+ protection provides shade and privacy for naps.
- Direct Attachment to Vista and Cruz Strollers: No adapters required, with additional adapters available for Minu and Ridge strollers.
- One-Hand Stroller Release Button: Carry handle allows for a smooth release from stroller to car.
Car Seat Specs:
- Age Range: Approximately 0–1 years
- Suitable for babies 4-35 lbs and up to 32” in height (whichever comes first)
- Car Seat Dimensions: 25.8” L x 17” W x 23” H
- Car Seat on Base Dimensions: 28” L x 17” W x 25” H
- Car Seat Base-Only Dimensions: 20.8” L x 14.5” W x 9.8–12.8” H (height adjusts from lowest to highest positions)
- True Weight: Car seat 9.9 lbs, base 9 lbs
Car Seat Base Features: SMARTSecure quick installation system, built-in lock-off for secure seatbelt installation, dual level indicators, and four-position adjustable foot for easy leveling.
This bundle is designed for convenience, safety, and comfort, providing everything you need for smooth transitions and peace of mind wherever your journey takes you.
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- Free Standard Shipping on $100+ Orders to the USA.
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4.6 ★★★★★
Based on 18 reviews
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Product Reviews
★★★★★ 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
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
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