SKU: 70130584240

Porterfield Brake Pads for 2000 LINCOLN TOWN CAR Limo, R/Drum Brakes

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Description

Porterfield Brake Pads for 2000 LINCOLN TOWN CAR Limo, R/Drum BrakesPorterfield Brake Pads for 2000 LINCOLN TOWN CAR Limo, R Drum Brakes 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

Porterfield Brake Pads for 2000 LINCOLN TOWN CAR Limo, R/Drum Brakes

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: 70130584240

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4.9 ★★★★★
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Verified Purchase
Richard Hackathorn
Cuba, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Lexington, 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
New York, 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
Los Angeles, 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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