SKU: 65665971872
maxi cosi bassinet iora air

maxi cosi bassinet iora air Maxi Cosi Iora Bedside Bassinet Classic Graphite

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Description

maxi cosi bassinet iora air Maxi Cosi Iora Bedside Bassinet Classic GraphitePremium Bedside Bassinet for Safe Room Sharing Newborns: The Maxi Cosi Iora Bedside Bassinet brings comfort, safety, and sophistication to your nursery, designed for parents who want their baby close during those precious first months. With adjustable height and proximity controls, the Iora customizes to fit your bed frame and lifestyle. Whether for nighttime nursing, quick 3 a. m. diaper changes, or the peace of mind of room sharing without bed

Premium Bedside Bassinet for Safe Room-Sharing Newborns: The Maxi-Cosi Iora Bedside Bassinet brings comfort, safety, and sophistication to your nursery, designed for parents who want their baby close during those precious first months. With adjustable height and proximity controls, the Iora customizes to fit your bed frame and lifestyle. Whether for nighttime nursing, quick 3 a.m. diaper changes, or the peace of mind of room-sharing without bed-sharing, the Iora provides a secure, independent sleeping space engineered to meet Consumer Product Safety Commission safe sleep guidelines. The foldable design means you can bring your baby's familiar sleep environment to grandma's house, hotel rooms, or weekend getaways.

How the Maxi-Cosi Iora Bedside Bassinet Works

The Iora adjusts through four distinct height positions to match standard platform beds, adjustable beds, and traditional frame beds. Three slide positions let you bring the bassinet closer for nighttime check-ins and feeding, or push it slightly away when you need space. The firm, flat mattress—engineered to meet CPSC safe sleep standards—stays protected under the bassinet's breathable mesh sides, which promote air circulation while keeping your newborn visible and within arm's reach throughout the night. The large under-bassinet storage basket holds diapers, wipes, pajamas, and other essentials so you never scramble in the dark. When travel calls, the entire bassinet folds flat in under a minute and packs into an included carry bag.

Key Features of the Iora

  • Customizable height: Four positions to match your bed height, from platform to raised frames
  • Proximity control: Three slide positions bring baby closer or create distance as needed
  • EcoCare fabric: 100% recycled plastic bottles, soft and breathable without added fire retardants
  • Breathable mesh sides: Promotes airflow and keeps you visually connected while baby sleeps
  • Safe sleep mattress: Firm, flat sleeping surface meets federal CPSC safe sleep standards
  • Storage basket: Under-bassinet organization for nighttime caregiving essentials within arm's reach
  • Portable design: Folds flat with included carry bag for effortless travel and storage
  • Easy care: Machine-washable mattress pad and carry bag on gentle cycle
  • Modern style: Six sophisticated color options (Truffle, Oat, Green, Graphite, Slate, Latte) complement any nursery
  • Secure locks: Height and slide locks prevent accidental shifting during sleep

When You Need the Maxi-Cosi Iora

Perfect for newborns through age 6 months (or until your baby reaches 20 lbs or can push up on hands and knees, whichever comes first). Ideal for parents practicing safe room-sharing, frequent nighttime feedings, and quick diaper changes without leaving your bedroom. The adjustable design and travel portability make it equally useful for grandparent stays, hotel visits, and families who move between homes. The Iora delivers the closeness of co-sleeping with the safety and independence of a dedicated sleep surface.

Safety & Quality Standards

Engineered by Maxi-Cosi with over 25 years of expertise in child sleep and travel safety. The Iora is designed with a firm, flat sleeping surface that meets Consumer Product Safety Commission guidelines for safe infant sleep. EcoCare fabric prioritizes your baby's health while breathable mesh sides reduce suffocation risks. The secure height and slide lock mechanisms prevent accidental shifting during the night. Always ensure both sides of the height adjust are locked at the same level before placing your baby down. Never add soft bedding, pillows, comforters, or extra padding—use only the mattress provided by the manufacturer. Always place your baby on their back to sleep. Do not use this product when your infant begins to push up on hands and knees or has reached 20 lbs (9.1 kg), whichever comes first. Consult the user guide for proper assembly and setup specific to your bed frame.

Explore the complete Maxi-Cosi collection at ANB Baby and find the right bassinet, car seat, and stroller combination for your newborn.

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Charlottesville, US
★★★★★ 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.
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Reviewed in the United States on April 1, 2019
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Verified Purchase
Par
Alexandria, 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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Verified Purchase
Richard Hackathorn
Lake Worth, 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
Whiting, 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
Port Orchard, 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

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