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Deep Learning 101 for Scientists and Engineers Authored by Yong-Jun Shin, a former biomedical engineering university professor and industry staff data scientist, this essential guide introduces deep learning to scientists and engineers new to the field. The book simplifies complex concepts of deep learning, particularly focusing on Transformers, which are at the forefront of technological innovation and pivotal in developments like ChatGPT. It emphasizes hands-on coding with PyTorch and avoids extensive mathematical complexities, favoring clear, conceptual explanations over technical depth. "Deep Learning 101" uniquely addresses the application of adaptive Transformers for real-time, dynamic environments that many scientists and engineers face, setting it apart from traditional deep learning texts. This approach enables professionals to effectively apply these techniques in fields as varied as environmental monitoring, autonomous systems, and personalized healthcare. Featuring practical examples, a focus on insightful explanations, and links to accessible online resources, this book is designed to equip non-specialists with the foundational knowledge needed to utilize deep learning effectively in their work. Whether you are a researcher, academic, or industry practitioner in fields ranging from biotechnology to industrial automation, this book provides the tools to innovate and enhance systems with advanced deep learning technology.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.