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ランドセルカバー 撥水・軽量タイプ 反射材(リフレクター)付き クジラと航海日誌1. 2. 3. A4 6() 4. 5.COLORFUL CANDY QUALITY COLORFUL CANDY QUALITY cm 55. 529 100% :
1.軽量・丈夫・速乾性。トップクオリティの新素材で快適さを追及！
快適さを追求した新素材は、シワになりにくく、耐久性もバツグン。今後もおしゃれ＆可愛い柄で登場予定です。
2.水を弾く素材だから、汚れに強くお手入れ簡単
水や液体が表面に弾いて滑り落ちるはっ水機能。汚れてもサッと拭くだけお手入れ簡単です。
3.A4フラットファイル対応ランドセル専用。ライトに反射して光る反射材付き
6年間使う大切なランドセルを雨やほこり、砂などの汚れからしっかりと守ってくれます。さらに夜間の安全性に配慮した反射材(リフレクター)を搭載しました。
4.毎日使うから、機能的でお手入れ簡単・便利
カバーの内側には、小物収納に便利なポケット付き。ハンカチやティッシュなどをすぐに取りだせます。
5.キレイなまま長期にわたって使える品質と、安全性。COLORFUL CANDY QUALITY
国際的なテスト機関で堅牢性・安全性確認済みの素材のみを使用。仕入れから製造・販売まで、リスクを入り込ませない一貫体制。キレイなまま長期にわたって使える品質と、安全性。それがCOLORFUL CANDY QUALITY。
快適さを追求した新素材は、シワになりにくく、耐久性もバツグン。今後もおしゃれ＆可愛い柄で登場予定です。
2.水を弾く素材だから、汚れに強くお手入れ簡単
水や液体が表面に弾いて滑り落ちるはっ水機能。汚れてもサッと拭くだけお手入れ簡単です。
3.A4フラットファイル対応ランドセル専用。ライトに反射して光る反射材付き
6年間使う大切なランドセルを雨やほこり、砂などの汚れからしっかりと守ってくれます。さらに夜間の安全性に配慮した反射材(リフレクター)を搭載しました。
4.毎日使うから、機能的でお手入れ簡単・便利
カバーの内側には、小物収納に便利なポケット付き。ハンカチやティッシュなどをすぐに取りだせます。
5.キレイなまま長期にわたって使える品質と、安全性。COLORFUL CANDY QUALITY
国際的なテスト機関で堅牢性・安全性確認済みの素材のみを使用。仕入れから製造・販売まで、リスクを入り込ませない一貫体制。キレイなまま長期にわたって使える品質と、安全性。それがCOLORFUL CANDY QUALITY。
サイズ（単位：cm）
タテ：約55.5／ヨコ：約29
※商品によってサイズに多少の誤差がございます。予めご了承ください。
素材：ポリエステル100% 裏地:ナイロン
●使用におけるご注意
※ポリエステルには汚れを吸収する特性があり、汚れが強いものと一緒に洗濯してしまうと生地が黒ずんでしまう場合があります。付着した汚れが強いものとは別に洗濯して下さい
※ポリエステルには防火性がないため、火を近づけると生地が溶けてしまう可能性があります。高温のアイロンでも変形・テカリが出る場合があります。使用する際はご注意下さい、
※乾燥機にかけると変形してしまう可能性があります。もともと乾きやすい生地なので自然乾燥がおすすめです。
※熱と一緒にシワをつけてしまうとなかなか取れないので、洗濯機の脱水や乾燥は短めにしてください。
※高温のお湯だと逆汚染が起こりやすくなりますので、ぬるま湯をおすすめします。
※ポリエステル生地は日光に強い素材ですが、濃い色のものは色落ち色あせしてしまうので陰干しがおすすめです。
※色の濃いものと一緒にお洗濯は避けて下さい。
※洗濯後、長時間放置しないで下さい。
※暑い場所で長期間、他の物と一緒に放置しているとプリントの色移りする可能性があります。
●洗濯について
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
※ポリエステルには汚れを吸収する特性があり、汚れが強いものと一緒に洗濯してしまうと生地が黒ずんでしまう場合があります。付着した汚れが強いものとは別に洗濯して下さい
※ポリエステルには防火性がないため、火を近づけると生地が溶けてしまう可能性があります。高温のアイロンでも変形・テカリが出る場合があります。使用する際はご注意下さい、
※乾燥機にかけると変形してしまう可能性があります。もともと乾きやすい生地なので自然乾燥がおすすめです。
※熱と一緒にシワをつけてしまうとなかなか取れないので、洗濯機の脱水や乾燥は短めにしてください。
※高温のお湯だと逆汚染が起こりやすくなりますので、ぬるま湯をおすすめします。
※ポリエステル生地は日光に強い素材ですが、濃い色のものは色落ち色あせしてしまうので陰干しがおすすめです。
※色の濃いものと一緒にお洗濯は避けて下さい。
※洗濯後、長時間放置しないで下さい。
※暑い場所で長期間、他の物と一緒に放置しているとプリントの色移りする可能性があります。
●洗濯について
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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4.2 ★★★★★
Based on 21 reviews
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Product Reviews
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid.
I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets.
As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before.
I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
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Reviewed in the United States on March 1, 2022