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Item #:
210001151
Transformers in Action
85% of respondents would recommend this to a friend
Item #:
210001151
IMP 74
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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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What Stands Out
Dynamic Transformations
Experience seamless transitions between robot and vehicle modes, enhancing play value and creativity for children and collectors alike.
Durable Design
Crafted with high-quality materials, these figures withstand rough play while maintaining intricate details, ensuring longevity and satisfaction.
Collectible Range
A diverse lineup of characters from the beloved franchise, appealing to both nostalgic fans and new enthusiasts, fostering community and collecting.
Product Details
| 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) |
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Toy Collectors
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Kids aged 6-12
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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.
Product Description
Transformers in Action
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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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Pros
- 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.
Cons
- Not suitable for casual readers without a background in machine learning.
Product Price History
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IMP 74
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Features & Benefits
- 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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