SKU: 41899307461

2012-2014 Mercedes-Benz C250 1.8L Automatic Sport Xenon HID Headlight Ballast Control Module 130732926301 725

$60.09 $66.77
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Description

2012-2014 Mercedes-Benz C250 1.8L Automatic Sport Xenon HID Headlight Ballast Control Module 130732926301 725Xenon HID Headlight Ballast Control Module 130732926301 7255724 Fit For BMW Feature: 1: According to the original factory specifications,perfect match for the original car. 2: Own different test machines to design exact accurate parameter for our products. All items were tested for performance. 3: Made by high quality material, lightweight, anti rust, colorfast and durable. 4: Aftermarket product with premium quality. 5: Stable performance, high

Xenon HID Headlight Ballast Control Module 130732926301 7255724 Fit For BMW

Feature:
1: According to the original factory specifications,perfect match for the original car.
2: Own different test machines to design exact accurate parameter for our products.All items were tested for performance.
3: Made by high quality material, lightweight, anti-rust, colorfast and durable.
4: Aftermarket product with premium quality.
5: Stable performance, high reliability,suitable for replacing your broken one.

Specifics:
Condition: 100% Brand New
Material: Metal
Color:show as pictures
Manufacturer Part Number: 63-11-7-356-250，63117356250，7356250
Interchange Part Number: 63-11-7-356-250，63117356250，7356250
Other Part Number: 130732931715
Type: Ballast
Voltage：12 V
Wattage：35 W
Features：Electric Ballast
Lighting Technology：Xenon
Fitment Type: Direct Replacement

Fitment:
For BMW 528i 2.0L Base 2014
For BMW 528i xDrive 2.0L Base 2014
For BMW 535d 3.0L DIESEL 6 cylinder Base 2014
For BMW 535d xDrive 3.0L DIESEL 6 cylinder Base 2014
For BMW 535i 3.0L 6 cylinder Base 2014
For BMW 535i xDrive 3.0L 6 cylinder Base 2014
For BMW 550i 4.4L V8 Base 2014
For BMW 550i xDrive 4.4L V8 Base 2014
For BMW M5 4.4L V8 Base 2014
For BMW ActiveHybrid 5 3.0L ELECTRIC/GAS 6 cylinder Base 2014

7 Series F01N F02N
For BMW 740i 3.0L 6 cylinder Base 2013-2015
For BMW 740Li 3.0L 6 cylinder Base 2013-2015
For BMW 740Li xDrive 3.0L 6 cylinder Base 2013-2015
For BMW 750i 4.4L V8 Base 2013-2015
For BMW 750i xDrive 4.4L V8 Bas 2013-2015
For BMW 750Li 4.4L V8 Base 2013-2015
For BMW 750Li xDrive 4.4L V8 Base 2013-2015
For BMW 760Li 6.0L V12 Base 2013-2015
For BMW ActiveHybrid 7 3.0L ELECTRIC/GAS 6 cylinder Base 2013, 2014

For BMW X3 F25
For BMW X3 2.0L xDrive28i 2015, 2016
For BMW X3 2.0L DIESEL xDrive28d 2015, 2016
For BMW X3 3.0L 6 cylinder xDrive35i 2015, 2016

For BMW X4 F26
For BMW X4 2.0L xDrive28i 2015, 2016
For BMW X4 3.0L 6 cylinder M40i 2016
For BMW X4 3.0L 6 cylinder xDrive35i 2015, 2016

For BMW X5 F15 F85 X5M
For BMW X5 2.0L ELECTRIC/GAS AWD xDrive40e 2016
For BMW X5 3.0L 6 cylinder AWD xDrive35i 2014-2016
For BMW X5 3.0L 6 cylinder DIESEL AWD xDrive35d 2014-2016
For BMW X5 3.0L 6 cylinder RWD sDrive35i 2014-2016
For BMW X5 4.4L V8 AWD M 2015, 2016
For BMW X5 4.4L V8 AWD xDrive50i 2014-2016

For BMW X6 F16 F86 X6M
For BMW X6 3.0L 6 cylinder xDrive35i 2015, 2016
For BMW X6 4.4L V8 M 2015, 2016
For BMW X6 4.4L V8 xDrive50i 2015, 2016

For BMW Z4 E89
For BMW Z4 2.0L A/T sDrive28i 2014-2016
For BMW Z4 3.0L 6 cylinder A/T sDrive35i 2014-2016
For BMW Z4 3.0L 6 cylinder A/T sDrive35is 2014-2016

For Mini R55 R55N R56 R56N R57 R57N R58 R59 R60 R61
For Mini Cooper 1.6L Automatic 2010-2014
For Mini Cooper 1.6L Automatic Clubman 2010-2014
For Mini Cooper 1.6L Automatic Coupe 2012-2014
For Mini Cooper 1.6L Automatic Coupe John Cooper Works 2013-2014
For Mini Cooper 1.6L Automatic Coupe S 2012-2014
For Mini Cooper 1.6L Automatic John Cooper Works 2013-2014
For Mini Cooper 1.6L Automatic John Cooper Works Clubman 2013-2014
For Mini Cooper 1.6L Automatic Roadster 2012-2014
For Mini Cooper 1.6L Automatic Roadster John Cooper Works 2013-2014
For Mini Cooper 1.6L Automatic Roadster S 2012-2014
For Mini Cooper 1.6L Automatic S 2010-2014
For Mini Cooper 1.6L Automatic S Clubman 2010-2014
For Mini Cooper 1.6L M/T 2010-2014
For Mini Cooper 1.6L M/T Clubman 2010-2014
For Mini Cooper 1.6L M/T Coupe 2012-2014
For Mini Cooper 1.6L M/T Coupe John Cooper Works 2012-2014
For Mini Cooper 1.6L M/T Coupe S 2012-2014
For Mini Cooper 1.6L M/T John Cooper Works 2010-2014
For Mini Cooper 1.6L M/T John Cooper Works Clubman 2010-2014
For Mini Cooper 1.6L M/T John Cooper Works GP 2013-2013
For Mini Cooper 1.6L M/T Roadster 2012-2014
For Mini Cooper 1.6L M/T Roadster John Cooper Works 2012-2014
For Mini Cooper 1.6L M/T Roadster S 2012-2014
For Mini Cooper 1.6L M/T S 2010-2014
For Mini Cooper 1.6L M/T S Clubman 2010-2014
For Mini Cooper Countryman 1.6L Automatic 2011-2014
For Mini Cooper Countryman 1.6L Automatic John Cooper Works ALL 2013-2014
For Mini Cooper Countryman 1.6L Automatic S 2011-2014
For Mini Cooper Countryman 1.6L Automatic S ALL 2011-2014
For Mini Cooper Countryman 1.6L M/T Base 2011-2014
For Mini Cooper Countryman 1.6L M/T John Cooper Works ALL 2013-2014
For Mini Cooper Countryman 1.6L M/T S 2011-2014
For Mini Cooper Countryman 1.6L M/T S ALL 2011-2014

Mercedes Models :
For Mercedes-Benz C250 1.8L Automatic 2012-2014
For Mercedes-Benz C250 1.8L Automatic Luxury 2012-2014
For Mercedes-Benz C250 1.8L Automatic Sport 2012-2014
For Mercedes-Benz C300 3.0L V 2012-2012
For Mercedes-Benz C300 3.0L V 2012-2012
For Mercedes-Benz C300 3.5L V 2013-2014
For Mercedes-Benz C300 3.5L V 2013-2014
For Mercedes-Benz C350 3.5L V 2012-2014
For Mercedes-Benz C350 3.5L V 2012-2014
For Mercedes-Benz C350 3.5L V 2012-2014
For Mercedes-Benz C63 AMG 2012-2014
For Mercedes-Benz C63 AMG 2012-2012
For Mercedes-Benz GL350 3.0L DIESEL V 2013-2014
For Mercedes-Benz GL450 4.6L V 2013-2014
For Mercedes-Benz GL550 4.6L V 2013-2014
For Mercedes-Benz GL550 4.6L V 2013-2014
For Mercedes-Benz GL63 AMG 2013-2014
For Mercedes-Benz GLK250 2.1L DIESEL Automatic Bluetec 2013-2014
For Mercedes-Benz GLK350 3.5L V 2013-2014
For Mercedes-Benz GLK350 3.5L V 2013-2014
For Mercedes-Benz ML350 3.0L DIESEL V 2012-2014
For Mercedes-Benz ML350 3.5L V 2012-2014
For Mercedes-Benz ML350 3.5L V 2013-2014
For Mercedes-Benz ML550 4.6L V 2012-2014
For Mercedes-Benz ML63 AMG 2012-2014
For Mercedes-Benz SL550 4.6L V 2013-2014
For Mercedes-Benz SL63 AMG 2013-2014
For Mercedes-Benz SL65 AMG 2013-2014

For GM Models :
For Cadillac XTS 2013-2017
For Cadillac CTS 2014-2019
For Chevrolet Camaro 2016-2018

For Volvo models :
For Volvo C30 2010-2013
For Volvo C70 T5 Convertible 2010-2013

Package Include:
2x Ballast
2x Bulb
2x Power Cable
6 x Screws
(show as pictures)
[* Instruction is Not included!]
（Strictly Tested , High Quality Products）

Note:
1.Please check the description or use the year/make/model check finder and replace part numbers to confirm the compatibility before purchasing.
2.Professional installation is recommended.

Warranty:
Guarantee Period: 1 Years
Returns & Refunds: Customers can return the goods within 60 days of receipt and process quick refund within one day
24-Hour Support: Provide you with timely after-sale support

Shipping Notes
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Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
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SKU: 41899307461

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4.9 ★★★★★
Based on 639 reviews
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Product Reviews
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Verified Purchase
0x00000000:00000000
Louisville, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Omaha, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Omaha, US
★★★★★ 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Houston, US
★★★★★ 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
A
Verified Purchase
Adam
Dallas, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

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