SKU: 36934138933

Porterfield Brake Pads for 2017 AUDI A6 3.0T

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Description

Porterfield Brake Pads for 2017 AUDI A6 3.0TPorterfield Brake Pads for 2017 AUDI A6 3. 0T Porterfield R4 Porterfield R4 Carbon Kevlar based brake pads were fully designed for heavy duty extreme motorsports use. Its one of the the few true motorsports competition pads available on the market today. The carbon based semi metallic R4 brake material allows the brake pad to absorb huge amounts of heat and dissipate it quickly and evenly over time. The Carbon Kevlar material also allows the brake pad

Porterfield Brake Pads for 2017 AUDI A6 3.0T

Porterfield R4

Porterfield R4 Carbon Kevlar based brake pads were fully designed for heavy-duty extreme motorsports use. Its one of the the few true motorsports competition pads available on the market today. The carbon based semi metallic R4 brake material allows the brake pad to absorb huge amounts of heat and dissipate it quickly and evenly over time. The Carbon-Kevlar material also allows the brake pad to heat up to operating temperature right away so little pad warmup is required for optimal operating condition. This compound also requires very little bed-in so that one is able to change out the pads and almost immediately able use them to their full potential.

R4 series provides high initial bite for immediate brake response while yielding extremely consistent modulation and predictability. This is great for all road courses, oval track, rally, vintage racing, autocross, club events, and professional driving events. This is one of the best motorsports pads we have used. Available for many applications, so please contact us if you do not see one for your car.

Porterfield R4-1

The R4-1 Vintage Full Race Compound was developed using knowledge testing in the vintage racing community. Optimum uses for the R4-1, under conditions where very high friction is needed with minimal warm up time and in applications where there is difficulty in maintaining sufficient heat with conventional race pad compounds. Widely used on vintage GT and formula cars the R4-1 is also gaining popularity in off-road and rally-cross classes. Great modulation, consistent pedal feedback and rotor friendly at all temperatures as with all the other Porterfield Carbon Kevlar compounds.

Porterfield R4-E

The R4-E Endurance Race Compound is a carbon kevlar compound made to last a bit longer than the original R-4 compound. The R4-E compound is designed to endure higher prolonged temperature and still has pad life as long or longer than Porterfield R-4 do. This pad is great for club enduro events and applications where temperatures are at their maximum.

Porterfield R4-S

Porterfield R4-S high performance street and autocross brake pads are great for heavy-duty street and light track applications. Compound is perfect for everyday use while still keeping the highly capable track ready performance. The R4-S features a high friction level that will increase your stopping power with minimal pedal effort.

The R4-S series pads are VERY VERY rotor friendly and yield very low levels of dust; levels are far below OEM equipment or any other high performance brake pads. Your car will stop better and your wheels will stay cleaner longer. Good for autocross, rallies, driving school, and of course daily driving with a little extra stopping power. R4-S pads are available for virtually all vehicles sold in the US and custom R4-S pad sizes for competition style calipers are also available.

This is one of the very best all around street/strip brake pads available at a great price.

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SKU: 36934138933

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4.9 ★★★★★
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A
Verified Purchase
Amazon Customer
Waukegan, 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
San Leandro, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Fort Morgan, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Belleville, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022
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Verified Purchase
Gabe Rigall
Phoenix, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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