SKU: 6300845492

Dark Knight Trilogy Batpod 1:12 Scale Model Kit Modoking

Sale price$28.34 Regular price$31.49
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Ships within 48 hours · Estimated delivery Jul 17 - Jul 22

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Description

Dark Knight Trilogy Batpod 1:12 Scale Model Kit ModokingModoking introduces their assembly range of Batman models kit inspired by the Dark Knight trilogy This release features a range of Batman figures, Bat Signal light and the Batpod The Batpod is designed with the suspended frame structure from the movie and capable of supporting the model from this release. We cannot guarantee compatibility to other models outside the Modoking release. All products are new and sealed with instructions. Some items are

Modoking introduces their assembly range of Batman models kit inspired by the Dark Knight trilogy

This release features a range of Batman figures, Bat-Signal light and the Batpod

The Batpod is designed with the suspended frame structure from the movie and capable of supporting the model from this release.

We cannot guarantee compatibility to other models outside the Modoking release.

All products are new and sealed with instructions. Some items are boxed and others in bags, please note images or message us for information on specific models.

  • Provided in parts frames
  • For ages 14+ due to small parts
  • Genuine Modoking release

Dimensions

  • Bat Pod 28.8x8.3cm
  • Figures approx. 17cm
Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
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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]
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SKU: 6300845492

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4.2 ★★★★★
Based on 6 reviews
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Z
Verified Purchase
Zay
Alexandria, 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
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Reviewed in the United States on October 23, 2024
R
Verified Purchase
Rory Derrick
Alexandria, 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.
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Reviewed in the United States on December 2, 2021
C
Verified Purchase
Cory P.
Port Orchard, 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
Carnegie, 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
Birmingham, 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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