Pay in installments of $45.21 with
,
and
Shipping Estimate
USA
- USA
- CAN
- USA
- CAN
Ships within 48 hours · Estimated delivery Aug 23 - Aug 28
Promo Codes Available:
For Your Every Summer RSVP, with Code: SUMMER15
Description
EBC Greenstuff 2000 Series Sport Brake Pads (DP21724)For spirited street use on lighter sports sedans and hot hatches, Greenstuff 2000 series is a perfect choice. As an eco friendly pad grade, EBC Greenstuff 2000 contains no lead, copper, or toxic sulfides to comply with all environmental legislation and is a direct replacement for your stock or original pads reducing stopping distances and improving brake response. Not ideal for track use, although many drivers have used Greenstuff 2000 for Hillclimb
For spirited street use on lighter sports sedans and hot hatches, Greenstuff 2000 series is a perfect choice.
As an eco-friendly pad grade, EBC Greenstuff 2000 contains no lead, copper, or toxic sulfides to comply with all environmental legislation and is a direct replacement for your stock or original pads reducing stopping distances and improving brake response.
Not ideal for track use, although many drivers have used Greenstuff 2000 for Hillclimb and lighter cars on the track. For even faster street driving you might consider the EBC Yellowstuff grade or for race use the EBC new Bluestuff or RP and SR series pads
Features:
- A medium to low dust material that offer a nominal 10-15% brake improvement
- Improved resistance to heat fade at higher speeds
- Suits modern Euro and Asian models alike
- ECE R90 approved for all cars that require the European legislation compliance
- Red brake-in surface coating accelerates pad brake in after install
- Features red “brake in’ surface coating for instant safe braking after install
- Pads made with the Nucap NRS hook technology to ensure 100% secure pad bond
Greenstuff™ – 2000 Series Brake Pads
EBC Greenstuff™ has a high initial brake effect and gives drivers confidence from first application of the brake pedal. There is no such thing as a no dust pad. The very nature of how a brake works requires that some dust be generated to avoid rotor vibration (shimmying). The EBC pad creates less dust and dust is more easily washed off than most. All pads create more dust as they are bedding in especially if rotors are worn. Nominal friction coefficient 0.55. For heavier faster cars we strongly recommend Redstuff which does offer much less dust.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.4 ★★★★★
Based on 22 reviews
Sort
Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
★★★★★ 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
★★★★★ 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
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
★★★★★ 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
recommand products
Wilwood Forged Dynalite P/S Park Brake Kit Drill-Zinc New Big Ford 2.50in Offset - 140-7140-Z
610.72
Wilwood Forged Dynalite P/S Park Brake Kit Polished New Big Ford 2.50in Offset - 140-7140-P
628.78
Wilwood Forged Dynalite P/S Park Brake Kit Ford 8.8 w/2.5in Offset-5 Lug - 140-7146
529.72
Wilwood Narrow Superlite 6R Front Kit 12.19in Drilled Red 63-87 C10 w/ Wilwood Pro Spindles - 140-15302-DR
667.97
Wilwood Dynapro Radial4 / MC4 Rear Kit 12.19 Drilled Red 2014-2015 Mini Cooper w/Lines & Cables - 140-15219-DR
1107.14