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Description
maxi cosi rodifix pro2 Maxi Cosi Rodifix Pro2 i-SizeNew!! Available to pre order. Call 01908 504 506 for more information. The upgraded RodiFix Pro i Size has three recline positions, so your child can sit back and relax in superior comfort and style for years. With the highest i Size safety standards and an adjustable open design which alters in both height and width, your child will always be safe and comfortable as they grow from 3. 5 up to 12 years old. Safety With ISOFIX connectors it's easy to
Safety
With ISOFIX connectors it's easy to install the RodiFix Pro² i-Size. And with no armrests to get in the way, buckling-up is always quick and hassle-free. So easy in fact, that independent children can buckle-up themselves. You may not be able to stop your child from growing up so fast, but you can keep up with them, as the seat's adjustable design grows both in height and width. Just press a button on the back of the headrest to adjust the height as they get taller and easily move the intuitive side wings out as they get bigger.
Ease of use
With ISOFIX connectors it’s easy to install the RodiFix Pro² i-Size. And with no armrests to get in the way, buckling-up is always quick and hassle-free. So easy in fact, that independent children can buckle-up themselves. You may not be able to stop your child from growing up so fast, but you can keep up with them, as the seat’s adjustable design grows both in height and width. Just press a button on the back of the headrest to adjust the height as they get taller and easily move the intuitive side wings out as they get bigger.
Age-range
At any age, children want to sit back and relax or sleep during car journeys. With some car seats this often turns into an awkward and uncomfortable nap as their unsupported heads nod forward, which also makes parents feel uncomfortable. Not with the RodiFix Pro² i-Size; the multi-age car seat offers three relaxing recline positions. The open, adjustable design, means that the seat grows with your child, providing more than eight years of comfort and safety, aged from 3.5 year old up to 12 years old.
Comfort features
RodiFix Pro² i-Size has three recline positions, which gives children the choice to either sit up straight or recline back slightly and sleep. The AirProtect® Safety cushions are not only protective, they're also extremely soft and provide even more cushioning. The upgraded RodiFix Pro² i-Size has extra padding in the seat for improved comfort and an extra wide backrest, to further enhance your child's comfort as they grow. Made using premium 100% recycled EcoCare fabric, RodiFix Pro² i-Size is distinguishable thanks to it's embossed branding details and premium stitching lines. The seat also features improved ClimaFlow ventilations panels, breathable foam and performance fabrics that enhance air circulation and keep your child at a comfortable temperature, at all times.
Features include:
Ease of use
- We know that you can’t stop your child from growing up so fast, but you can keep up with them, as the seat’s adjustable design grows both in height and width. Just press a button on the back of the headrest to adjust the height as they get taller, and easily move the intuitive side wings out as they get bigger.
- With no armrests to get in the way, buckling-up is always quick and hassle-free. So easy in fact, that independent children can buckle-up themselves. No hassle for the parents anymore!
Comfort
- With different, relaxing recline positions, your child will always be cosy and comfy when you're on the move, regardless of whether they’re sitting up and looking out of the window or sat back and sleeping.
- Special ClimaFlow panels and breathable fabrics encourage air circulation, to ensure your child is always comfortable and at the right temperature.
- RodiFix Pro² i-Size provides the highest level of comfort for your child due to the soft padding under the fabric.
Safety
- No compromise. The RodiFix Pro² i-Size is packed with protective features. It’s built to today’s highest i-Size safety standards and has high wings on both sides for increased Side Impact Protection.
- Adds an extra built-in guard and provides maximum protection with improved 3D impact absorption, from multiple angles to ensure your child’s safety.
- Patented AirProtect® side impact protection in the headrest reduces the risk of head injury by up to 20% and cushioning enhances comfort for your child.
- RodiFix Pro² i-Size is fitted quickly and effortlessly into any car, via ISOFIX installation.
General
- Grows with your child both in height and width for the best comfort, so it's nice to know you'll get more than eight years' use with the RodiFix Pro i-Size.
- This car seat for children enables you to fit up to three kids in one row. It has a narrow design but is still comfortable due to the in width adjustable side wings.
- The RodiFix Pro² i-Size combines the best-in-class comfort, due to the relaxed recline positions, with planet-friendly innovation to create a sustainable car seat using EcoCare, which means it has 100% recycled fabrics.
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4.3 ★★★★★
Based on 1951 reviews
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Product Reviews
★★★★★ 5
Holds a decent amount of jewelry!
Color: Carbonized Brown, Color: Carbonized Brown
I was quite impressed with this little jewelry box. Although it is on the smaller side, it utilizes every bit of the storage space available really well. I’d ultimately love to get a bigger armoire- as it is, this jewelry box contains what I wear most often, but I have a larger collection than this particular jewelry box can hold- my plan is to find a larger jewelry armoire that resembles what my mother had because I loved that one and then passed this one down to my daughter who loves it.
For its size, it does absolutely hold a lot. I definitely underestimated how much it would hold. I love that there are drawers and well. I would love to see the ring area hinged so that I don’t have to reposition it when I’m done grabbing my rings, I think it’s a really cool, unique way to approach that particular area. I love that every little bit at this jewelry box is designed to have utility. I hate wasting space and time and I love good organization so it’s been really nice being able to pack as much as I can in there. The top opens up to space for earrings and other miscellaneous items. There are both open and more structured components. And the space for bracelets rotates, which is really nice- I didn’t realize that it rotated and I was a little bit worried that I was gonna constantly knock things down while I was reaching through or something. There is lots of room inside both doors for necklaces, and it fits a lot more than I thought it would.
The wood stain is a really pretty kind of ashy natural stain- the sort of grey tint is really nice and it’s gorgeous. I’m not a huge fan of mirrors as far as the front goes, but I do have an artist in house who is really good at coming up with stuff for this, just a little ways to put art in your every day, so I’ll probably have her paint over. The jewelry box also doesn’t take much space up at all. While I am looking for something with a little bit larger footprint, I don’t necessarily want to waste a bunch of real estate in the meantime so I’m really pleased with how compact it is.
This is a great little jewelry box - as I mentioned it doesn’t house all of my jewelry, but that’s because my collection is mostly heirloom and I don’t want to take it out from where it is right now. If it were larger, I would probably do so but for now it just houses my everyday items and a little bit extra. I think it’s great and I’m super happy with it!
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Reviewed in the United States on March 17, 2026
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience.
If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference.
I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance:
The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals).
Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning.
Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time.
And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning.
There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s.
The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read.
There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning.
The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique.
Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017