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yugioh evolution starter deck yugi Yugioh Orica - Starter Deck: Yugi Evolution (SYE)This deck includes all the cards you see in the pictures (You can check a full cardlist as well. You can choose between 8 different card backing styles. These cards cannot be played in official tournaments and have the only purpose to be collectibles, or they can be used as tokens. The size is exactly as the official cards, common rarity. Every card will have its own sleeve, and they will be shipped in a bubble envelope to avoid damages. Shipping is
This deck includes all the cards you see in the pictures (You can check a full cardlist as well.
You can choose between 8 different card backing styles.
These cards cannot be played in official tournaments and have the only purpose to be collectibles, or they can be used as tokens.
The size is exactly as the official cards, common rarity.
Every card will have its own sleeve, and they will be shipped in a bubble envelope to avoid damages.
Shipping is from Italy
Cardlist:
SYE-001 Dark MagicianSYE-002 Mystical Elf
SYE-003 Feral Imp
SYE-004 Winged Dragon, Guardian of the Fortress #1
SYE-005 Summoned Skull
SYE-006 Beaver Warrior
SYE-007 Gaia the Fierce Knight
SYE-008 Celtic Guardian
SYE-009 Great White
SYE-010 Giant Soldier of Stone
SYE-011 Mystic Clown
SYE-012 Neo the Magic Swordsman
SYE-013 Gazelle the King of Mythical Beasts
SYE-014 Warrior Dai Grepher
SYE-015 Dark Blade
SYE-016 Wall of Illusion
SYE-017 Man-Eater Bug
SYE-018 Sangan
SYE-019 Kuriboh
SYE-020 Giant Rat
SYE-021 Sonic Bird
SYE-022 Zombyra the Dark
SYE-023 Royal Magical Library
SYE-024 Black Luster Soldier
SYE-025 Black Luster Ritual
SYE-026 Dark Hole
SYE-027 Dian Keto the Cure Master
SYE-028 Fissure
SYE-029 Monster Reborn
SYE-030 Change of Heart
SYE-031 Last Will
SYE-032 Card Destruction
SYE-033 Remove Trap
SYE-034 The Reliable Guardian
SYE-035 Axe of Despair
SYE-036 Malevolent Nuzzler
SYE-037 Mystical Space Typhoon
SYE-038 Mystic Plasma Zone
SYE-039 Swords of Revealing Light
SYE-040 Pot of Greed
SYE-041 Trap Hole
SYE-042 Waboku
SYE-043 Magic Jammer
SYE-044 Seven Tools of the Bandit
SYE-045 Spellbinding Circle
SYE-046 The Eye of Truth
SYE-047 Backup Soldier
SYE-048 Shift
SYE-049 Disappear
SYE-050 Raigeki Break
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4.2 ★★★★★
Based on 30 reviews
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Product Reviews
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning.
There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s.
The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read.
There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning.
The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique.
Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry.
The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff!
If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper.
As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture.
So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money.
The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018