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Lambar abu:
88573717
PyTorch実践入門 ~ ディープラーニングの基礎から実装へ
92% of respondents would recommend this to a friend
Lambar abu:
88573717
XAF 23757
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Yi Oda yanzu, a samu nan da Juma'a, Oktoba 02
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The definitive implementation of deep learning by PyTorch!
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What Stands Out
Comprehensive Coverage
Offers a thorough introduction to deep learning concepts, from basic principles to advanced implementations, making it suitable for beginners and experienced practitioners alike.
Hands-On Practice
Includes practical exercises and projects that encourage readers to apply their knowledge, enhancing understanding and retention of PyTorch and deep learning techniques.
Accessible Language
Written in clear, approachable language, ensuring complex topics are easy to understand, which helps readers build confidence in their deep learning journey.
Bayanin samfur
- PyTorchによるディープラーニング実装の決定版! ディープラーニングの重要な基礎概念と、PyTorchを用いたディープラーニングの実装方法について、細部まで掘り下げて解説。限られたデータでニューラルネットワークを訓練する方法、訓練済みモデルのデプロイ方法など『ディープラーニング・プロジェクトのベストプラクティス』を提示します。・ディープラーニングのメカニズムを解説・Jupyter Notebook上でサンプルコードを実行・PyTorchを用いたモデル訓練の実施・実データを使用するプロジェクトをベースに実践的解説・本番環境へのさまざまなモデルデプロイ方法PyTorchで実際にどのように組み込まれて実現されているのか、細部まで掘り下げた解説をしていますのでディープラーニングの活用を目指している開発者や詳しく知りたい方におすすめです。Manning Publications『Deep Learning with PyTorch』の翻訳書第1部 PyTorchの基礎第1章 ディープラーニングとPyTorchの概要第2章 訓練済みモデルの利用方法第3章 PyTorchにおけるテンソルの扱い方第4章 さまざまなデータをPyTorchテンソルで表現する方法第5章 ディープラーニングの学習メカニズム第6章 ニューラルネットワーク入門第7章 画像分類モデルの構築第8章 畳み込み(Convolution)第2部 ディープラーニングの実践プロジェクト:肺がんの早期発見第9章 肺がん早期発見プロジェクトの解説第10章 LUNAデータをPyTorchデータセットに変換第11章 結節候補を画像分類するモデルの構築第12章 評価指標とデータ拡張を用いたモデルの改善第13章 セグメンテーションを用いた結節の発見第14章 結節・腫瘍解析システムの全体を構築第3部 デプロイメント(Deployment)第15章 本番環境にモデルをデプロイする方法
| Publisher | マイナビ出版 |
| Publication date | January 30, 2021 |
| Language | Japanese |
| Print length | 608 pages |
| ISBN-10 | 4839974691 |
| ISBN-13 | 978-4839974695 |
| Item Weight | 880 g |
| Dimensions | 7.28 x 1.18 x 9.25 inches (18.5 x 3 x 23.5 cm) |
Who Should Buy?
-
Beginner Data Scientists
Perfect for those new to data science and looking to understand deep learning concepts using PyTorch.
-
Students in AI Courses
Ideal for students enrolled in artificial intelligence courses seeking practical experience with deep learning frameworks.
-
Self-learners in Tech
Great resource for individuals self-studying deep learning who want hands-on practice with real-world examples.
-
Advanced Developers
Not suitable for experienced developers who already possess deep knowledge of PyTorch and deep learning techniques.
KWATANCEN ABUN
Kuna da wata tambaya? Yi taɗi da mu
Tambayoyi & Amsoshi Abokin Ciniki
-
Tambaya:
What is the main focus of this book?
Amsa: The book focuses on teaching deep learning concepts and implementations using PyTorch. -
Tambaya:
Is prior knowledge of Python or machine learning required?
Amsa: While some familiarity with Python is helpful, the book provides a comprehensive explanation suitable for beginners. -
Tambaya:
What kind of projects does this book include?
Amsa: The book includes practical projects, such as an early detection model for lung cancer, along with deployment strategies.
Eli Stevens , Luca Antiga , Thomas Viehmann Electricity & Communications Editorial Review
The product, a Japanese-language book on deep learning with PyTorch, has received a mix of positive and critical feedback from customers. Many reviewers find it to be an excellent resource for beginners and recommend it for those just starting with deep learning or PyTorch. They praise the book for its clear explanations, well-structured content, and practical approach to teaching image object recognition, which is a central topic in the text. The inclusion of source code on Git adds significant value, allowing readers to verify their understanding as they progress through the material. However, some users highlight that while the book is beginner-friendly, it may become less practical for those looking to apply the concepts more broadly, mentioning that the book can feel overly focused on specific applications without providing enough context for broader understanding. Additionally, a few reviews indicate that the explanations of certain functions are insufficient and that some technical jargon may be overwhelming for readers without a strong programming background. The flow of some sections appears to be influenced by English structuring, making it slightly less readable for a Japanese audience. Overall, while the text is deemed essential for those serious about learning deep learning with PyTorch, it seems best suited for intermediate users who already have a foundational understanding of programming concepts. **
Customer Reviews & Ratings
57 Ƙimar da abokan ciniki suka bayar
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5 Tauraruwa
47%
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4 Tauraruwa
31%
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3 Tauraruwa
12%
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2 Tauraruwa
6%
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1 Tauraruwa
4%
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Ribobi
- Clear, detailed explanations that are beginner-friendly.
- Practical focus on image object recognition.
- Availability of GitHub code for practical application.
- Strong foundational emphasis on deep learning principles.
Fursunoni
- May be too focused on specific applications for broader learning.
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XAF 23757
Yi Oda yanzu, a samu nan da Juma'a, Oktoba 02
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Abu 14 ne suka rage.
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Fasaloli & Fa'idoji
- Comprehensive guide to deep learning concepts and PyTorch.
- Practical project-based learning with real data.
- Best practices for training neural networks with limited data.
- Step-by-step implementation instructions for model training.
- Various methods for deploying models to production environments.
- Ideal for developers looking to enhance their deep learning skills.
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