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Description
double adult stroller Bumbleride Indie Twin Double Stroller OceanStay on the move with 2. The Bumbleride Indie Twin side by side double stroller keeps you on the move and outside doing what you love. Known as a parents lifeline with multiple kids, its side by side seat design gives you a smooth and balanced push and allows you to customize the seats to your needs with quick access to each. Have twins? Add dual car seats, dual bassinets or simply convert seats to infant mode with no attachments. Ready to use at
Stay on the move with 2. The Bumbleride Indie Twin side-by-side double stroller keeps you on the move and outside doing what you love. Known as a parent’s lifeline with multiple kids, its side-by-side seat design gives you a smooth and balanced push and allows you to customize the seats to your needs with quick access to each. Have twins? Add dual car seats, dual bassinets or simply convert seats to infant mode with no attachments. Ready to use at birth. Debuting in 2024 with a 100% natural and nontoxic cork handle. Responsibly made and manufactured. (see eco and health checklist).
Highlights:
- Narrow silhouette, fits through a standard doorway
- Balanced & smooth push on air-filled tires with all-wheel suspension
- Quick, compact fold with auto-lock & standing stow
- Spacious canopies offer ample sun coverage UPF 45+
- Individual seats give options for different age ranges
- Health checklist: Non-toxic OEKO-TEX certified fabrics. Free from PFAS, Fire Retardant, PVC, BPA, Phthalates, Polyurethane Foam, Chlorine, Vinyl and Formaldehyde
Eco Checklist:
- Adjustable, non-toxic handlebar made of all-natural cork
- PFAS free durable water repellent
- Eco Fabric made from OEKO-TEX Standard 100 certified 100% recycled PET (90 plastic water bottles per stroller)
- Black colorway uses an innovative solution dye process that conserves approximately 25-40 gallons of water per stroller
- Dusk colorway uses a soft-to-the-touch, poly/wool blend for the interior seat lining certified by the Responsible Wool Standard
- 50% of plastic frame components are sourced from recycled fishing nets
- Plastic free packaging
Indie Twin Tech Specs:
- Recommended Use: Birth – 90 lb. (combined total weight)
- NEW, adjustable, non-toxic, anti-microbial Cork handle with safety strap included
- Adjustable footrests for infant option and deep footwell for toddlers
- Independent seats and canopies for customized comfort
- UPF 45+ canopies with pop-out extension for extra coverage + air vents
- Eco-fabrics free from harmful chemicals including lead, PVC, fire retardants, phthalates and polyurethane foam (100% rPET + OEKO-TEX Standard 100 Certified)
- PFC free durable water repellent (DWR free of PFOA, PFOS & PFASs)
- One step hinged fold with ergonomic, trigger release and auto-lock
- Hot-shoes pre-installed for sue with adapters and bassinets
- One-handed backrest reclines
- Built in storage pockets for water bottles and snacks
- Large cargo basket with storage for air pump (pump included)
- Air-filled tires (12”) and all wheel suspension
- 5-point breakaway harnesses with shoulder pads
- Machine washable seat fabrics + canopies (all removable)
- 360-degree swivel front wheel with in-line lock option for all-terrain
- Lightweight and durable aluminum frame
What’s Included
- 3 Year Warranty
- Air-pump
- Wrist strap
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4.8 ★★★★★
Based on 11 reviews
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Product Reviews
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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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.
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Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
★★★★★ 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.
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Reviewed in the United States on March 1, 2022
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt.
PROS:
- Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way.
- Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops.
- Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages.
- Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them.
- PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly.
CONS:
- Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste.
- Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
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Reviewed in the United States on February 26, 2022