SKU: 58007022651

Jaguar/Land Rover / 2014-2019 / Intercooler Boost Pipe 2.0L Diesel G4D3-6C780-AA

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Jaguar/Land Rover / 2014-2019 / Intercooler Boost Pipe 2.0L Diesel G4D3-6C780-AAMany of our parts can fit a wide range of vehicles! Not sure if this will fit your vehicle? Please contact us with your registration or vin number! Same day dispatch on orders before 1pm Mon Fri Product Description Delivery & Returns Payment Information Terms & Conditions Product Description This unit comes as a single complete unit without any additional seals, gaskets or fitting accessories. As a used part it has signs of wear and tear but is still


Many of our parts can fit a wide range of vehicles!
Not sure if this will fit your vehicle?

Please contact us with your registration or vin number!
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Product Description

This unit comes as a single complete unit without any additional seals, gaskets or fitting accessories. As a used part it has signs of wear and tear but is still 100% fully functional, as such this unit is supplied with eBay's 30-Day Money Back Guarantee.

Please view the photos and part numbers carefully prior to purchase.

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Tel: 01246 231 500

Environment Agency Permit Number(s) YP3192ZY/A001 & NE/3179/002

For an extended version of our terms and conditions relating to postage, warranty periods and returns please visit the "About" Section of our DragonAutoPartsUK Store.

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UK Mainland and International Postage

  1. Flexible Shipping Options: You can choose from a range of shipping options subject to the size and weight of your order, you can even opt for a priority morning delivery if needed!

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Returns
  1. Personalized Assistance: When you start a return, we advise you to call us on 01246 231 500 to speak with one of our representatives. We can help you determine if you're eligible for a return and offer advice on how to proceed.

  2. Photo Evidence: If your order arrived in a damaged condition, we require photographic evidence to prove the state in which it arrived. This helps to speed up the return process and ensures that we can resolve the issue as quickly as possible.

  3. Convenient Collection: If your order was damaged outside of your control, we will arrange a collection on your behalf. We'll require you to verify a collection address to send our courier to, making the process as convenient as possible for you.

  4. Clear Communication: If the returned item is larger or more complex, such as an engine, gearbox, or other complex units, we'll send it to a third party for a full assessment and cause of failure. During this period, which lasts between two to four weeks, we'll keep you informed every step of the way and we'll always notify you of their findings.

  5. Secure Policy: We unfortunately do not accept international returns unless the buyer pays the return postage fees or the part is damaged on delivery, both of which require prior authorization before being returned to us. All products are discreetly security marked, so please do not try to send back a faulty replacement or wrong part that is not the exact unit you received from us.

    For an extended version of our terms and conditions relating to postage, warranty periods and returns please visit the "About" Section of our DragonAutoPartsUK Store.

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Terms and Conditions Summary

These terms and conditions are a shortened version of our full T&C's. For an extended version of our terms and conditions relating to postage, warranty periods and returns please visit the "About" Section of our DragonAutoPartsUK Store. All products are supplied with individual warranties, please refer to listing details or contact us for more information. Our warranties do not cover labour or incidental expenses arising from fault.

All products are supplied with individual warranties, please refer to listing details or contact us for more information. Our warranties do not cover labour or incidental expenses arising from fault. All used parts are sold in good working order with a minimum 30-day warranty. Always view the listing pictures carefully, if you have any queries regarding a listing or purchased product, please contact us prior to bidding / buying / returning. We pride ourselves on our customer service and in the rare event that you have a problem with your item we will always be happy to help. For the vast majority of cases, we will be able to resolve the dispute with a simple refund or exchange unit. All parts sold must be fitted in accordance with manufacturer specifications and the fitting process must be conducted at an approved VAT registered garage and performed by a fully qualified mechanic. DragonAutoPartsUK reserves the right to reject an application due to insufficient evidence or in the event of a misdiagnosis and incorrect fitting.

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4.9 ★★★★★
Based on 952 reviews
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0x00000000:00000000
Fort Morgan, 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.
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Reviewed in the United States on April 18, 2017
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Zygerian99
Bozeman, 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.
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Reviewed in the United States on January 21, 2020
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Verified Purchase
Shannon
West Palm Beach, 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!!
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Reviewed in the United States on November 30, 2025
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Verified Purchase
William P Ross
Bozeman, 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
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Verified Purchase
Adam
Lake Worth, 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.
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Reviewed in the United States on May 22, 2026

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