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- Transformers in Action
Transformers in Action
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KRW 122755
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Transformers in Action gives data scientists and machine-learning engineers a clear and practical roadmap to how transformer models and large-language-model (LLM) architectures work under the hood.
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제품 세부 정보
| Publisher | Manning Publications |
| Publication date | November 25, 2025 |
| Language | English |
| Print length | 256 pages |
| ISBN-10 | 1633437884 |
| ISBN-13 | 978-1633437883 |
| Item Weight | 1.04 pounds (470 grams) |
| Dimensions | 7.38 x 0.64 x 9.25 inches (18.7 x 1.6 x 23.5 cm) |
Who Should Buy?
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Toy Collectors
Collectors will appreciate the intricate designs and limited editions, enhancing their existing Transformer memorabilia.
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Kids aged 6-12
This product provides interactive play and promotes creative thinking, making it ideal for children within this age group.
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Fans of Action
Action enthusiasts will enjoy the dynamic transformations and combat scenarios, adding excitement to playtime.
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Younger Children
Children under 6 may struggle with complex transformations and small parts, posing a potential safety hazard.
제품 설명
Transformers in Action
고객 질문 및 답변
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Ubuy에서 온라인으로 Transformers in Action 쇼핑하는 방법?
답변: Ubuy에서 온라인으로 Transformers in Action을(를) 쉽게 쇼핑할 수 있습니다.. 제품을 검색하고 체크아웃하는 동안 배송 방법을 선택하고 해당 위치로 배송받기만 하면 됩니다. -
의문:
Transformers in Action는 South Korea에서 온라인 쇼핑이 가능합니까?
답변: 예, Ubuy South Korea에서 이 제품을 합리적인 가격에 구매할 수 있습니다.. Transformers in Action은(는) 현지에서 구할 수 없지만 특급 배송 서비스로 저희를 신뢰할 수 있습니다. -
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답변: 주문한 제품의 배송 시간은 주문한 항목과 선택한 배송 방법에 따라 다릅니다.. 예상 배송 시간은 결제 과정에서 언급되므로 안심하고 쇼핑하십시오.
Natural Language Processing Editorial Review
**** Nicole Koenigstein’s "Transformers in Action" emerges as a quintessential resource for both new and seasoned machine learning practitioners eager to immerse themselves in the transformative architecture of transformers. Unlike many existing resources that either veer into overly abstract mathematics or remain firmly entrenched in basic API calls, this book strikes a delicate balance, offering a deep yet accessible exploration of the underlying principles driving today's leading language models. One of the standout features of the book is its architectural clarity. Koenigstein meticulously unpacks the attention mechanism responsible for the success of transformers, enabling readers to understand both the theory and practical application. The book’s extensive coverage of advanced topics, such as decoding strategies and preference alignment, marks it as a valuable resource for anyone aiming to apply these methodologies effectively in real-world scenarios. Moreover, the text shines in its discussion of engineering best practices and real-world Constraints, addressing critical areas like parameter-efficient fine-tuning and quantization that practitioners encounter frequently. The focus on multimodal and specialized models is forward-thinking, directing attention to current industry trends in AI and complex document processing. The book does not shy away from ethical Considerations, rounding out its practical advice with a chapter dedicated to bias detection and interpretability. This holistic approach ensures that readers not only learn to build powerful models but do so in a responsible manner. Practical implementation is greatly facilitated by the inclusion of well-crafted Jupyter notebooks and a GitHub repository, which provide valuable hands-on experience through real-world coding examples. This practical aspect reinforces the book's intention not merely as an informational text but as a guide for actionable learning. In conclusion, "Transformers in Action" serves as a critical tool for data scientists and machine learning engineers alike, combining rigorous mathematical insights with hands-on coding practices. It excels in clarity and comprehensiveness, making it a must-read for anyone looking to deepen their understanding of transformers and their application in AI. **
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장점
- Architectural clarity and practical explanations.
- Comprehensive coverage of advanced topics in transformers.
- Focus on real-world engineering challenges and optimization strategies.
- Forward-looking discussions on multimodal and specialized models.
- Ethical Considerations on bias detection and model interpretability.
- High-quality hands-on resources, including Jupyter notebooks and GitHub repository.
단점
- Not suitable for casual readers without a background in machine learning.
Product Price History
중요 정보
- 제한 사항: 국제 배송되는 제품의 경우 제조업체 보증이 유효하지 않을 수 있으며, 제조업체 서비스 옵션을 사용하지 못하거나 제품 설명서, 지침 및 안전 경고가 대상 국가 언어로 표시되지 않을 수 있으며, 제품(및 첨부 자료)은 도착 국가 표준, 사양 및 라벨링 요구 사항에 따라 설계되지 않을 수 있으며, 제품은 목적지 국가 전압 및 기타 전기 표준을 준수하지 않을 수 있습니다(가능한 경우 어댑터 또는 변환기 사용 필요). 수령인은 제품을 도착 국가로 합법적으로 수입할 수 있는지 확인할 책임이 있습니다. Ubuy 또는 그 제휴사에서 주문할 때, 수령인은 기록 수입자로서 도착 국가의 모든 법률과 규정을 준수해야 합니다.
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특징 및 장점
- Learn the practical applications of transformer models and LLM architectures.
- Hands-on examples and code samples for real-world implementation.
- Gain skills to build, fine-tune, and optimize transformer-based models.
- Make informed architecture-level decisions for effective AI systems.
- Focus on responsible AI practices and ethical considerations.
- Equipped for both beginners and experts looking to deepen their knowledge.
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