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Description
uppababy car seats UPPAbaby MESA V2 Infant Car SeatMESA v2. See how simple. UPPAbaby designed the MESA with intuitive innovations you can see and features that remove the guesswork. The MESA maximizes ease to minimize error, which allows parents to move forward with confidence and security. Suitable 4 35 lbs and up to 32 in height MESA Safety SMARTSecureTM system: Make installation quick and simple by combining our innovative SMARTSecure system, auto retracting LATCH design, red to green tightness
MESA v2. See how simple.
UPPAbaby designed the MESA with intuitive innovations you can see and features that remove the guesswork. The MESA maximizes ease to minimize error, which allows parents to move forward with confidence and security.
Suitable 4-35 lbs and up to 32″ in height
MESA Safety
- SMARTSecureTM system: Make installation quick and simple by combining our innovative SMARTSecure® system, auto-retracting LATCH design, red to green tightness indicator, and four-position adjustable foot.
- Tightness indicator: It’s as easy as red to green. When the tightness indicator turns green, it provides confirmation that the seat is installed correctly whether using LATCH or belt.
- Auto-retracting LATCH: Unlike traditional LATCH connectors, MESA’s unique retracting LATCH system tightens effortlessly, minimizing installation error.
- Protection from all sides: The adjustable headrest is reinforced with EPP foam to help absorb energy while the integrated design of our Side Impact Protection keeps baby’s head protected during a collision. Additionally, the 4th rebound carry handle position allows for increased safety and aids in absorbing rebound forces.
- Optimized for preemies 4 lbs+: The removable infant insert is designed to provide better fit and body positioning for babies approximately 4 – 11 lbs.
- European Routing: European Routing provides a safe and secure carrier-only installation when the use of a base isn’t convenient. The belt path easily secures the carrier at three points of contact to reduce movement.
MESA Convenience
- Fire retardant free fabrics: The MESA V2 is made with fabric that is free from fire retardant chemicals for all fashions and textiles, including the large UPF 25+ hideaway canopy. It is also GREENGUARD® GOLD Certified to support healthier air quality and low chemical emissions.
- Buckle up with confidence: The simple one-crotch buckle position and no-rethread, adjustable harness makes buckling up a breeze.
- Performance travel system: For the ultimate in portability and safety, MESA V2 directly attaches to both the VISTA® and CRUZ® strollers. It is even compatible with the MINU® and RIDGE® with the addition of convenient adapters. This creates a stylish and simple solution that goes from drive to stroll (and back again).
- Expandable options: Whether you’re having twins or this is your second or third time around, the MESA paired with the VISTA can accommodate your growing family.
- Quick release button: A quick press of the button on the top of the carrier handle releases it from the frame so you can go from stroller to car in one seamless motion. Additionally, the convenient, reversible option allows baby to face you while strolling, or you can turn the carrier to pull right up to a table for mealtime.
- No-rethread harness: As your infant grows, the MESA infant car seat adapts. MESA’s unique no-rethread five-point harness is adjustable from the front, allowing for proper fitting in just seconds.
- Removable and washable fabric and canopy: It’s a fact of life -- babies dribble and drool. MESA's seat fabric and canopy fabric is easily removable for cleanup without the need to disassemble the harness.
- Hideaway canopy with SPF 25+: The extra-large integrated, extendable canopy provides more protection outside and conveniently tucks away when not in use.
- Level indicators on both sides of base: Whether you are installing from the left or right side of the car, level indicators confirm that your seat is properly leveled.
- Finished base bottom: Smooth, finished base bottom minimizes indents and eliminates the need for a seat protector.
Specifications
- Suitable 4-35 lbs and up to 32″ in height, whichever comes first
- Infant insert with wedge recommended for preemies and smaller newborns 4-11 lbs.
Dimensions
- Infant Car Seat: 17”W x 25.8”L x 23”H
- Car Seat on Base: 17”W x 28”L x 25”H
- Base: 14.5”W x 21.3” L x 10.3” H
- Carrier weight: 9.9 lbs
- Base weight: 9 lbs
Includes
Infant car seat, car seat base and infant insert (for use with children 4-11 lbs)Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
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]
- Please click here for more details>>> Return & Exchange Policy
4.9 ★★★★★
Based on 18 reviews
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Product Reviews
★★★★★ 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
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026