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Description
ICON Rebound HD 18x9 8x180 12mm Offset 5.5in BS 124.2mm Bore Bronze WheelICON Alloys is pround to announce new 18x9" sizes of the popular 'Rebound' wheel designed for optimal fitment on todays popular midsize, full size and heavy duty pickup trucks. Available in five, six and eight lug patterns and with application specific fitment dimensions, these new wheels yield a 2,750 pound load rating for the five and six lug versions and a 4,000 pound load rating on the eight lug version, maintaining ICON Alloys commitment to
ICON Alloys is pround to announce new 18x9" sizes of the popular 'Rebound' wheel designed for optimal fitment on todayβs popular midsize, full-size and heavy-duty pickup trucks. Available in five, six and eight lug patterns and with application-specific fitment dimensions, these new wheels yield a 2,750-pound load rating for the five and six-lug versions and a 4,000-pound load rating on the eight-lug version, maintaining ICON Alloysβ commitment to producing wheels that deliver style and strength.This Part Fits:
| Year | Make | Model | Submodel |
|---|---|---|---|
| 2020-2024 | Chevrolet | Silverado 2500 HD | Custom |
| 2015-2024 | Chevrolet | Silverado 2500 HD | High Country |
| 2011-2024 | Chevrolet | Silverado 2500 HD | LT |
| 2011-2024 | Chevrolet | Silverado 2500 HD | LTZ |
| 2011-2024 | Chevrolet | Silverado 2500 HD | WT |
| 2024 | Chevrolet | Silverado 2500 HD | ZR2 |
| 2015-2024 | Chevrolet | Silverado 3500 HD | High Country |
| 2011-2024 | Chevrolet | Silverado 3500 HD | LT |
| 2011-2024 | Chevrolet | Silverado 3500 HD | LTZ |
| 2011-2024 | Chevrolet | Silverado 3500 HD | WT |
| 2020-2024 | GMC | Sierra 2500 HD | AT4 |
| 2024 | GMC | Sierra 2500 HD | AT4X |
| 2015-2021 | GMC | Sierra 2500 HD | Base |
| 2011-2024 | GMC | Sierra 2500 HD | Denali |
| 2024 | GMC | Sierra 2500 HD | Denali Ultimate |
| 2022-2024 | GMC | Sierra 2500 HD | Pro |
| 2011-2024 | GMC | Sierra 2500 HD | SLE |
| 2011-2024 | GMC | Sierra 2500 HD | SLT |
| 2011-2014 | GMC | Sierra 2500 HD | WT |
| 2020-2024 | GMC | Sierra 3500 HD | AT4 |
| 2015-2021 | GMC | Sierra 3500 HD | Base |
| 2011-2024 | GMC | Sierra 3500 HD | Denali |
| 2024 | GMC | Sierra 3500 HD | Denali Ultimate |
| 2022-2024 | GMC | Sierra 3500 HD | Pro |
| 2011-2024 | GMC | Sierra 3500 HD | SLE |
| 2011-2024 | GMC | Sierra 3500 HD | SLT |
| 2011-2014 | GMC | Sierra 3500 HD | WT |
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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
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Reviewed in the United States on October 23, 2024
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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.
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Reviewed in the United States on December 2, 2021
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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
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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
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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