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Description
Wheel Bearing Rear 45x80x37mm for BMW 5 - 426321Vehicle Fitment & Part Details The Wheel Bearing Rear is listed for the BMW 5 (1995 2003). Size: 37mm; Dimensions: 45X80X37MM. Confirm compatibility by matching the listed fitment details. Verify VIN engine. Key Details SKU 426321 Component Wheel Bearing Specs Size: 37mm; Dimensions: 45X80X37MM Primary Fitment BMW 5 (1995 2003) Listing Highlights Listed for selected BMW applications shown in the fitment table. Size: 37mm; Dimensions: 45X80X37MM.
Vehicle Fitment & Part Details
The Wheel Bearing Rear is listed for the BMW 5 (1995-2003). Size: 37mm; Dimensions: 45X80X37MM. Confirm compatibility by matching the listed fitment details. Verify VIN/engine.
Key Details
- SKU
- 426321
- Component
- Wheel Bearing
- Specs
- Size: 37mm; Dimensions: 45X80X37MM
- Primary Fitment
- BMW 5 (1995-2003)
Listing Highlights
- Listed for selected BMW applications shown in the fitment table.
- Size: 37mm; Dimensions: 45X80X37MM.
Vehicle Fitment
Includes 31 supplied applications covering BMW. Use the full table below to confirm model, chassis, year range, engine and power before ordering.
View Full Vehicle Fitment (31 applications)
| Make | Model | Chassis | Years | Engine | Power |
|---|---|---|---|---|---|
| BMW | 5 | E39 | 1995-2003 | 523 i M52 B25 256S3; M52 B25 256S4 | 125 kW / 170 HP |
| BMW | 5 | E39 | 1995-2003 | 525 d M57 D25 256D1 | 120 kW / 163 HP |
| BMW | 5 | E39 | 1995-2003 | 525 i M54 B25 256S5 | 141 kW / 192 HP |
| BMW | 5 | E39 | 1995-2003 | 528 i M52 B28 286S1; M52 B28 286S2 | 142 kW / 193 HP |
| BMW | 5 | E39 | 1995-2003 | 530 d M57 D30 306D1 | 142 kW / 193 HP |
| BMW | 5 | E39 | 1995-2003 | 530 i M54 B30 306S3 | 170 kW / 231 HP |
| BMW | 5 | E39 | 1995-2003 | 540 i M62 B44 448S1; M62 B44 448S2 | 210 kW / 286 HP |
| BMW | 5 | E39 | 1995-2003 | M5 S62 B50 508S1 | 294 kW / 400 HP |
| BMW | 5 | E60 | 2001-2010 | 520 d N47 D20 A; N47 D20 C | 130 kW / 177 HP |
| BMW | 5 | E60 | 2001-2010 | 523 i N52 B25 A; N52 B25 B; N52 B25 BE | 130 kW / 177 HP |
| BMW | 5 | E60 | 2001-2010 | 523 i N52 B25 A; N52 B25 BE | 140 kW / 190 HP |
| BMW | 5 | E60 | 2001-2010 | 523 i N53 B25 A | 140 kW / 190 HP |
| BMW | 5 | E60 | 2001-2010 | 525 i M54 B25 256S5 | 141 kW / 192 HP |
| BMW | 5 | E60 | 2001-2010 | 525 i N52 B25 A; N52 B25 B; N52 B25 BF | 160 kW / 218 HP |
| BMW | 5 | E60 | 2001-2010 | 525 xi N52 B25 A | 160 kW / 218 HP |
| BMW | 5 | E60 | 2001-2010 | 530 d M57 D30 306D2 | 160 kW / 218 HP |
| BMW | 5 | E60 | 2001-2010 | 530 d M57 D30 306D2; M57 D30 306D3 | 155 kW / 211 HP |
| BMW | 5 | E60 | 2001-2010 | 530 d M57 D30 306D3 | 173 kW / 235 HP |
| BMW | 5 | E60 | 2001-2010 | 530 i M54 B30 306S3 | 170 kW / 231 HP |
| BMW | 5 | E60 | 2001-2010 | 530 i N52 B30 A; N52 B30 BF | 190 kW / 258 HP |
| BMW | 5 | E60 | 2001-2010 | 530 i N52 B30 A; N52 B30 BF | 200 kW / 272 HP |
| BMW | 5 | E60 | 2001-2010 | 530 i N53 B30 A | 200 kW / 272 HP |
| BMW | 5 | E60 | 2001-2010 | 535 i N54 B30 A | 225 kW / 306 HP |
| BMW | 5 | E60 | 2001-2010 | 545 i N62 B44 A | 245 kW / 333 HP |
| BMW | 5 | E60 | 2001-2010 | 550 i N62 B48 B | 270 kW / 367 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 523 i M52 B25 256S3; M52 B25 256S4 | 125 kW / 170 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 525 i M54 B25 256S5 | 141 kW / 192 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 528 i M52 B28 286S1; M52 B28 286S2 | 142 kW / 193 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 530 d M57 D30 306D1 | 142 kW / 193 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 530 i M54 B30 306S3 | 170 kW / 231 HP |
| BMW | 5 Touring | E39 | 1996-2004 | 540 i M62 B44 448S1; M62 B44 448S2 | 210 kW / 286 HP |
Fitment Notes
- Always match vehicle details before ordering. Verify VIN/engine.
Compatibility Verification Notes
- Confirm compatibility using VIN, engine code, chassis / platform, OE reference and original part comparison before ordering.
- Match the supplied fitment details to your vehicle, including model, year range and any listed engine or chassis information.
- Fitment data may vary by production date, market, import history and engine variant.
- This listing is for part identification and compatibility checking only. Installation must be carried out according to the vehicle manufacturer's service information by a suitably qualified person.
Common Questions
Will the Wheel Bearing Rear fit my BMW 5?
This part (426321) is listed for the fitments shown on this page. Confirm by matching the fitment details. Verify VIN/engine.
How do I confirm fitment if there are multiple variants?
Compare the supplied fitment details, original part details and any listed variant information before ordering. Verify VIN/engine.
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Exchange/Return Notes
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4.4 ★★★★★
Based on 21 reviews
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Product Reviews
★★★★★ 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
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry.
The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026