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ファブリックパネル Mサイズ (33cm×33cm) フルールS (18cm26cm) M (33cm33cm) 3 S1826cmM3333cmL51. 573cm 1 2 cm 3333 100% 1 2
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※製品に使われているパネルは木材です。パネルによっては濃淡が出てる部分があったり節が見えている場合がありますが、木材の性質によるものですので、返品・交換の対象外となります。
※柄の出具合はご指定できません。
ナチュラルなオックス生地で作られたファブリックパネル。
お手軽にお部屋が華やかになるおしゃれなインテリアアイテム。
ムダな加工を一切施していない美しいオックスファブリックは、ポスターや写真とは違った優しい空間を演出してくれます。豊富なラインナップから、お好みの柄を選んでみませんか。
（サイズにより、お作りしている柄数が違いますのでご確認ください）
■サイズは3タイプ
お気軽にちょこっと楽しめるSサイズ（18×26cm）、どの場所にも飾りやすいMサイズ（33×33cm）、お部屋の雰囲気をイメージチェンジできるLサイズ（51.5×73cm）
■飾り方は自由自在に
壁に掛けるのはもちろん置いたりしても楽しめます。
リビングや玄関・トイレなど、見慣れた壁にちょこっと飾るだけで雰囲気が変わります。
同サイズで柄を変えて並べてみたり、同柄でサイズ違いをコーディネイトしたりなど、お好みで楽しんでいただけます。（ティースハンガー1個 ネジ2個付き）
ひとつひとつ心を込めて丁寧に、日本で製造しました。全てハンドメイドなので安心してご自宅に飾れます。
ギフトとしてもお求めやすいので、おすすめのアイテムです。
※製品に使われているパネルは木材です。パネルによっては濃淡が出てる部分があったり節が見えている場合がありますが、木材の性質によるものですので、返品・交換の対象外となります。
※柄の出具合はご指定できません。
サイズ（単位：cm）
タテ：約33／ヨコ：約33
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素材：綿100% 木製パネル ティースハンガー1個 ネジ2個
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洗濯により若干の色落ち、濡れた状態での接触により色移りすることがございます。洗濯の際は、他のものとまとめて洗うのはお避け下さい。
●柄の出方について
柄の出方は、生地の裁断により、一点一点異なります。あらかじめご了承ください。
●商品仕様について
商品は写真と異なる場合や同等品へ仕様変更する場合がございます。予めご了承ください。
また、お揃い生地商品が完売の際はご了承ください。
その他のご注意点はこちら
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4.4 ★★★★★
Based on 25 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