SKU: 34812377276
jogger double stroller

jogger double stroller Baby Jogger City Select 2 Convertible Single to Double Stroller

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Description

jogger double stroller Baby Jogger City Select 2 Convertible Single to Double StrollerThe Baby Jogger City Select 2 Stroller is a versatile dream for parents who know having only one option is not an option. The Eco Collection contains Tencel fibers with ecosoft technology and leatherette handlebar and belly bar. With 24 configurations, this stroller converts from a single to a double with a second seat or infant car seat, or even to a triple stroller with a glider board! [Second seat and glider board sold separately.] It compactly

The Baby Jogger® City Select® 2 Stroller is a versatile dream for parents who know having only one option is not an option. The Eco Collection contains Tencel™ fibers with ecosoft technology and leatherette handlebar and belly bar.

With 24 configurations, this stroller converts from a single to a double with a second seat or infant car seat, or even to a triple stroller with a glider board! [Second seat and glider board sold separately.] It compactly folds 20% smaller than the market-leading single-to-double stroller, And the lightweight design, telescoping handlebar, and ample storage space provide even more convenience for your everyday travels. Customize your City Select® 2 with a variety of accessories to create the all-in-one stroller that keeps your family going, even as it grows.

Features:

  • Leatherette perforated handle grip​ and belly bar​
  • Stylish colour blocking [colour canopy, black seat pad]
  • Sustainability meets comfort with soft, breathable, thermal-regulating TENCEL™ fabric, which is used on the canopy and padded seat
  • Telescoping, height-adjustable handlebar provides comfortable steering and control, all within an arm's reach
  • Front-wheel suspension with never-flat front swivel wheels and all-terrain tires
  • Seat-back storage compartment and a large under-seat storage basket that holds up to 15 lb, so you always have everything you need within reach
  • Adjustable seat recline and calf support helps you find the most comfortable ride for your baby
  • Extended UV 50+ canopy with peekaboo window keeps kids shaded while allowing you to easily check-in Limited lifetime manufacturer's warranty on frame
  • Create a travel system by adding an infant car seat with adapters

Specifications:

  • Suitable for children up to 45 lbs
  • Dimensions: 23.8" W x 41.1" H
  • Stroller Weight: 26.71 lbs

Additional Information:

What is the Eco Collection?
  • The new City Select 2 contains Tencel™ fibres with ecosoft technology ​which used in clothing and sheets ​and is now found in canopy & seat pads
  • Sourced from responsibly managed forests.​
  • Soft, breathable & thermal regulating to keep your growing family cool and comfortable.​
  • Tencel™ fibres are unfavourable for bacteria growth.​

    ​Compatibility:

    • Create a travel system by adding a Baby Jogger, Britax, Chicco, Cybex, Graco, Maxi-Cosi, Nuna, Peg Perego, infant car seat with adapters [sold separately; compatible infant car seat models vary by brand]
    Shipping Notes
    • Free Standard Shipping on $100+ Orders to the USA.
    • Except Preorder products are shipped in 48 hours.
    • Delivery to the USA:
    1. 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
    SKU: 34812377276

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    4.0 ★★★★★
    Based on 19 reviews
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    Verified Purchase
    Richard Hackathorn
    Pawtucket, 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.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on February 26, 2022
    A
    Verified Purchase
    Amazon Customer
    San Leandro, US
    ★★★★★ 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
    K
    Verified Purchase
    Kindle Customer
    San Leandro, US
    ★★★★★ 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
    T
    Verified Purchase
    Tommy Jonsson
    Alexandria, US
    ★★★★★ 5
    Cover many areas in detail and recommendations for more to read for what's outside
    Format: Paperback
    Good book!
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on May 4, 2026
    M
    Verified Purchase
    Moses Kayanda
    Massapequa, US
    ★★★★★ 5
    One of the best machine learning books...
    Format: Paperback, Format: Paperback
    Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on March 1, 2022

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