![DIXCEL BRAKE PAD TYPE Z FRONT 2513092-Z [Compatibility List in Desc.]](https://cdn.shopify.com/s/files/1/0644/3486/4197/files/main_ph_z_f78fc4e4-4e99-43c0-8c68-7faaca57c4f2.jpg?v=1742327072)
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DIXCEL BRAKE PAD TYPE Z FRONT 2513092-Z [Compatibility List in Desc.]Brand DIXCEL Product Number 2513092 Z Compatible Car #REF! Product Description Model Grade:. 3. 2 GTA _x000D_ _x000D_ Z type _x000D_ All Round Performance Pad _x000D_ Being an multi use brake pad, the Z type can be for street use to circuit use _x000D_ Consistent braking even when the brakes are cold, something that is a must for street use brake pads. Also helps for a longer discs life. _x000D_ Stable braking power and high fade resistance even at
| Brand | DIXCEL |
| Product Number | 2513092-Z |
| Compatible Car | #REF! |
| Product Description | Model Grade:. 3.2 GTA _x000D_ _x000D_ Z type _x000D_ All Round Performance Pad _x000D_ Being an multi-use brake pad, the Z-type can be for street-use to circuit-use _x000D_ Consistent braking even when the brakes are cold, something that is a must for street-use brake pads. Also helps for a longer discs life. _x000D_ Stable braking power and high fade resistance even at high brake temperatures of race circuit usage . _x000D_ Perfect for high performance radial tires _x000D_ Friction Materials: Graphite metallic _x000D_ Effective Temperature Range: 0 ~ 850 °C _x000D_ Max u: 0.67 _x000D_ Ave u: 0.57 _x000D_ Min u: 0.50 _x000D_ Applicable Stages: Street, Circuit, Auto-cross, Off-road Compatible Vehicles: ALFAROMEO 156 932AXB 03/11 ~ ALFAROMEO 156 SPORT WAGON 932BXB 03/11 ~ ALFAROMEO 147 937AXL 03/10 ~ ALFAROMEO 159 93922 08/03 ~ ALFAROMEO 159 93932 06/02 ~ < br> ALFAROMEO BRERA 93922S 09/09 ~ ALFAROMEO BRERA 93932S 06/04 ~ ALFAROMEO GT 93732L 04/06 ~ 12/04 ALFAROMEO SPIDER 93932S 06/10 ~ LANCIA KAPPA 98/6 ~ 02 LANCIA KAPPA 94 ~ 02 |
| Remarks | VET No 0588973 → ALFAROMEO 156 932AXB 03/11 ~: VET No 0588973 ALFAROMEO 156 SPORT WAGON 932BXB 03/11 ~: right handle · AT vehicles (VET No 0588973 → ALFAROMEO 147 937AXL 03/10 ~: Fr. DISC 330x32m ALFAROMEO 159 93922 08/03 ~: VET No → 7115379 Bremb ALFAROMEO 159 93932 06/02 ~: VET No → 7115379 Bremb ALFAROMEO BRERA 93922S 09/09 ~: Bremb ALFAROMEO BRERA 93932S 06/04 ~: Bremb ALFAROMEO GT 93732L 04/06 ~ 12/04: Bremb ALFAROMEO SPIDER 93932S 06/10 ~: LANCIA KAPPA 98/6 ~ 02: < br> LANCIA KAPPA 94 ~ 02: Bremb |
| Notes | Images are for illustration purpose only. Actual product may vary. |
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4.2 ★★★★★
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★★★★★ 5
Before GPT can chat, it has to learn.
Format: Paperback
One of the most comprehensive guides to AI and deep learning available. All concepts covered clearly and concisely with illustrations. Complex concepts are broken down into understandable terms. Even though ML and AI require some complex math, you won’t need it to get idea of what’s going on inside the computer’s “brain”.
I use this as a reference when teaching AI concepts and preparing presentations.
I highly recommend it
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Reviewed in the United States on April 5, 2025
★★★★★ 5
Great examples and practical explanations
Format: Paperback
Excellent book. Highly recommend
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Reviewed in the United States on May 19, 2025
★★★★★ 5
Great for intuitive understanding
Format: Paperback
Amazing book. Great examples and diagrams. If you're looking to get an intuitive grasp of deep learning, look no further. If you're an engineer looking to apply it, I would recommend pairing this with one of the more technical canonical texts and a programming focused book.
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Reviewed in the United States on May 4, 2024
★★★★★ 5
Taught me backpropagation
Format: Paperback
I'm finding this book a great reference to supplement Syracuse IST 691 Deep Learning in Practice. In particular, its explanation of backpropagation is the clearest I have found yet, and that even includes 3 Blue 1 Brown, which always does excellent explanations.
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Reviewed in the United States on April 13, 2025
★★★★★ 5
An Easy Read
Format: Paperback
A good narrative description of how the various systems work. Good for getting a conceptual understanding. No math to speak of, and if you want it, you can refer to the references cited, or your other favorite machine learning book.
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Reviewed in the United States on May 7, 2024