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King Vw V8/V10 R8/RS6/Huracan (Size 0.25) pMaxKote Coated Main Bearing Set

Marsoni M251S
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King Vw V8/V10 R8/RS6/Huracan (Size 0.25) pMaxKote Coated Main Bearing SetKing Vw V8 V10 R8 RS6 Huracan (Size 0. 25) pMaxKote Coated Main Bearing Set This Part Fits: Year Make Model Submodel 2007 2011 Audi A6 Quattro Avant 2007 2011 Audi A6 Quattro Base 2007 2014 Audi A8 Quattro Base 2007 2014 Audi A8 Quattro L 2012 Audi A8 Quattro L W12 2007 2010 Audi Q7 Base 2007 2010 Audi Q7 Premium 2010 Audi Q7 Prestige 2009 2010 Audi Q7 TDI 2008 2012,2014 2015,2017 2018,2020 Audi R8 Base 2012 Audi R8 GT 2012 Audi R8 GT Spyder 2020 2022
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4.3 ★★★★★
Based on 638 reviews
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Zay
Phoenix, US
★★★★★ 5
Upside down (Great)
Format: Hardcover
Was extremely relatable and a blessing I gifted this book and want it back lol but I will be ordering again
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on October 23, 2024
R
Verified Purchase
Rory Derrick
Grantham, US
★★★★★ 5
Good book
Format: Paperback
Still working through it but I have no complaints. I have a shelf of no starch books and have not been disappointed by any. Some, of course, are better than others but this is a good book.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 2, 2021
C
Verified Purchase
Cory P.
Bozeman, US
★★★★★ 5
Dive into Algorithms is more like eating a Parfait than trying understand algorithms!
Format: Paperback
Seriously though great writing and explanation along with great history lessons. Also highly recommend for anyone working in the data science field.
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Reviewed in the United States on August 3, 2021
F
Verified Purchase
Frank Gonzalez
Battle Creek, US
★★★★★ 3
Ok content, not great explanations (2.5 our of 5 stars)
Format: Paperback
I found the contents of this book to be simply ok: either too simple, or the more complicated algorithms and concepts would not be as carefully explained as they should have been. It is not a terrible book, but it feels as though the author did not go over many drafts/iterations of this work.
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Reviewed in the United States on April 7, 2024
I
Ira Laefsky
Pawtucket, US
★★★★★ 5
An Excellent Self-Discovery Approach to Learning Algorithms
Format: Paperback
I have an Ivy League Master's Degree in Computer Science although it was accomplished 35 years ago. Of course, I had to complete an ACM-type course in Algorithms and Data Structures on the undergraduate and graduate level and managed to by rote accomplish enough to satisfy these courses. But until seeing this great book I never had the feeling of gaining an understanding of the approach to learning and building algorithms and the extent to which it is an important component of all programs. By a journey of guided self-discovery the author shows, not only the necessity of algorithms and their canonical forms, but a path to understanding the construction of algorithms to accomplish common and not so common practical problems. These range from the simple to understand, e.g. implementing Russian Peasant Multiplication, to advanced and up to date topics like Machine Learning. The highest praise I can give this book is that as a journey of guided self-discovery it produces an understanding in the reader of the process of constructing and understanding these algorithms and their place in all programming.
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Reviewed in the United States on February 7, 2021

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