BERTによる自然言語処理入門: Transformersを使った実践プログラミング
BERTによる自然言語処理入門: Transformersを使った実践プログラミング
You will be able to master BERT, the standard model for natural language processing!
BERTによる自然言語処理入門: Transformersを使った実践プログラミング
Item #: 87967488

BERTによる自然言語処理入門: Transformersを使った実践プログラミング

Item #: 87967488
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You will be able to master BERT, the standard model for natural language processing!
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What Stands Out

Practical Programming
Offers hands-on coding examples to reinforce concepts, making the complexities of Natural Language Processing accessible to learners and developers alike.
Cutting-edge Technology
Focuses on the Transformers architecture, ensuring readers stay informed about the latest advancements in Deep Learning and NLP for competitive edge.
Comprehensive Coverage
Covers BERT in-depth, providing readers with a thorough understanding of its applications, which helps bridge the gap between theory and practical usage.

Product Details

Shop BERTによる自然言語処理入門: Transformersを使った実践プログラミング online at a best price in Puerto Rico. 427422726X
  • 自然言語処理の標準モデル、BERTを使いこなせるようになる!BERTはGoogleが2018年末に発表した自然言語処理モデルです。「文脈」を考慮した処理が特徴的であり、言語理解を評価する11個のタスクについて最高精度を達成し、今や標準的なモデルとしての地位を確立しています。本書は、自然言語処理の近年における発展に大きな役割を果たし、かつ応用上も有用であるBERTの入門書です。前半で自然言語処理や機械学習について概説したのち、BERTによって実際にさまざまなタスクを解いていきます。具体的には、文章分類・固有表現抽出・文章校正・類似文章検索・データの可視化を扱います。データセットの処理から、ファインチューニング(BERTを特定の言語タスクに特化させるための学習)、性能の評価までの一連の流れを体験することで、BERTを自分で使えるようになることを目標とします。なお、BERTで処理を行うためのライブラリとして、深層学習の言語モデルを扱ううえでよく使用されるTransformersを、学習や性能評価を効率的に行うためのライブラリとしてPyTorch Lightningを用います。本書ではTransformersやPyTorch Lightningを用いたことがない読者を想定して、その使い方を一から体系的かつ丁寧に解説します。▼本書の環境言語:Python深層学習フレームワーク:PyTorchライブラリ:Transformers, PyTorch Lightning計算環境:Google Colaboratory▼本書の特徴・BERTで実際にさまざまなタスクを解くことができます。・使用するデータセットを日本語で統一しています。・ライブラリの使い方を一から体系的に説明します。
Publisher オーム社
Publication date June 28, 2021
Language Japanese
Print length 200 pages
ISBN-10 427422726X
ISBN-13 978-4274227264
Item Weight 500 g
Dimensions 7.32 x 0.63 x 9.33 inches (18.6 x 1.6 x 23.7 cm)

Who Should Buy?

Suitable For
  • Aspiring Developers

    Perfect for individuals looking to develop practical programming skills using BERT and Transformers for NLP applications.

  • AI Enthusiasts

    Ideal for those interested in artificial intelligence and natural language processing, seeking to understand cutting-edge technologies.

  • Students and Researchers

    Beneficial for academic learners focusing on NLP who require hands-on experience with modern machine learning techniques.

Not Suitable For
  • Complete Beginners

    Not suitable for individuals without any prior programming or NLP knowledge, as it may be too advanced.

Product Description

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ストックマーク株式会社 (Editor), 近江 崇宏 Theoretical Computer Science Editorial Review

**** "Introduction to Natural Language Processing by BERT: Practical Programming with Transformers" presents a nuanced exploration of natural language processing (NLP) through the lens of the BERT model. The book effectively introduces key concepts such as named entity recognition, text classification, and sentence correction, making it accessible for individuals familiar with the basics of Python and coding. Readers appreciate how the material is laid out through practical exercises using actual datasets, turning the learning process into an engaging experience. However, the book may not be entirely suitable for absolute beginners, as some reviews suggest that prior knowledge in NLP and programming is essential to fully grasp the content. Those with a solid foundation find the book particularly valuable, while complete novices may feel overwhelmed by the shorthand explanations and practical focus. Furthermore, experienced practitioners seeking in-depth mathematical insights into BERT might find the coverage lacking. Readers also note that the book functions well as an introductory resource to build foundational knowledge, from which they can explore more advanced materials. Some have pointed out that while its approach is practical and insightful, it can feel somewhat superficial if the reader is looking for thorough theoretical discussions. Ultimately, while the book is commended for clarity and practical applications, it may fall short for those seeking comprehensive theoretical frameworks or for those just starting in the field. **

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Pros

  • Accessible explanations for those with some prior knowledge in NLP and Python.
  • Engaging practical exercises using real datasets that enhance the learning experience.
  • Good coverage of core NLP tasks, particularly in the context of the Japanese language.

Cons

  • Not suitable for complete beginners; prior knowledge is essential.

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