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ゆうパケット 【メーカーお取り寄せ】 超長綿先染ブロードロンスト・白×紫ストライプ太 50先染ブロード生地1=10cm50cm(5) :550cm 101m 810 cm 108 100% 1. 5m(15) 1. 2.1. 5m 3. 4. 5.300 1=10cm 50cm10cm :550cm 101m () 2m 1m2=2m () PC + 0. 5cm1cm
数量：1=10cm単位での価格です。最低50cmから(数量：5)の販売となります。
※入力例:数量5→50cm/数量10→1m
※入力例:数量5→50cm/数量10→1m
ご注文・ご入金確認後、8～10営業日以内に発送予定です。（土・日・祝を除く）
ご注文いただきましたタイミングによっては、メーカー在庫がなく納期よりお時間がかかる場合がございます。その場合は、ストアよりご連絡しまして、当ストアへ入荷、手配でき次第、発送いたします。
ご注文いただきましたタイミングによっては、メーカー在庫がなく納期よりお時間がかかる場合がございます。その場合は、ストアよりご連絡しまして、当ストアへ入荷、手配でき次第、発送いたします。
こちらの商品は、生地以外の他商品と同時購入された場合、別送となります。
こちらの生地は商用利用可能です。
※生地耳に商用利用不可と記載されている場合がございますが、商用利用可能です。
※生地耳に商用利用不可と記載されている場合がございますが、商用利用可能です。
白地に少し太めのラインが等間隔に並んだ伝統的なロンドンストライプ。ふんわりとしパープルカラーが優雅で上品です。
高品質な「超長綿」を使用し、通常のブロードよりも細い糸で織り上げたました。先染めならではの深い色味と光沢があり、肌触りが良いのが特長の薄手素材。ブラウスやワンピース、パジャマなどの服地や、巾着などの小物にもおすすめです。
サイズ（単位：cm）
生地巾：約108
生地品質：綿100%
※オックス、ツイル等、1.5m(個数15)までのご注文に限り、ゆうパケットでのお届けが可能です。
1.ゆうパケットはポスト投函でのお届けとなります。ポストに入らない場合は、持ち帰ります。
2.厚さ制限があるため、オックス、ツイル等1.5m以上・キルティング、ラミネートのご注文は宅急便でのお届けになります。
3.配達日時のご指定はできません。
4.代金引換は対応しておりません。
5.料金は全国一律送料300円となります。
1.ゆうパケットはポスト投函でのお届けとなります。ポストに入らない場合は、持ち帰ります。
2.厚さ制限があるため、オックス、ツイル等1.5m以上・キルティング、ラミネートのご注文は宅急便でのお届けになります。
3.配達日時のご指定はできません。
4.代金引換は対応しておりません。
5.料金は全国一律送料300円となります。
ご購入について
数量：1=10cm単位での価格です。
販売は最低50cmから、10cm単位でカットいたします。
※入力例:数量5→50cm/数量10→1m
中切れについて
在庫状況によってはごくまれにですが中切れしている(途中で切れている)商品がございますのでご了承くださいます様お願い申し上げます。
〔ご注文内容〕 2m → 〔納品形態〕 1m×2枚=2m
※中切れ(途中で切れている状態)でのご納品になる場合は出荷前に弊社よりお客様にご連絡させていただきます。
●生地色について
生地および商品の画像は、できるだけ商品に近い色で掲載しております。同じ色名でも生地や商品によって明るさや鮮やかさなど色味が異なります。
※お客様のモニター設定やPCの機種、室内環境等により、色味に違いが発生してしまう場合もございます。
●お取扱いにおけるご注意
洗濯により若干の色落ちや、多少の縮みが発生する場合があります。
商品によっては+-0.5cm～1cmの誤差が発生してしまう場合がございます。
また、お揃い生地商品が完売の際はご了承ください。
数量：1=10cm単位での価格です。
販売は最低50cmから、10cm単位でカットいたします。
※入力例:数量5→50cm/数量10→1m
中切れについて
在庫状況によってはごくまれにですが中切れしている(途中で切れている)商品がございますのでご了承くださいます様お願い申し上げます。
〔ご注文内容〕 2m → 〔納品形態〕 1m×2枚=2m
※中切れ(途中で切れている状態)でのご納品になる場合は出荷前に弊社よりお客様にご連絡させていただきます。
●生地色について
生地および商品の画像は、できるだけ商品に近い色で掲載しております。同じ色名でも生地や商品によって明るさや鮮やかさなど色味が異なります。
※お客様のモニター設定やPCの機種、室内環境等により、色味に違いが発生してしまう場合もございます。
●お取扱いにおけるご注意
洗濯により若干の色落ちや、多少の縮みが発生する場合があります。
商品によっては+-0.5cm～1cmの誤差が発生してしまう場合がございます。
また、お揃い生地商品が完売の際はご了承ください。
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4.3 ★★★★★
Based on 21 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