Robinson emma c (9 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Zustand: New. Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.…

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Zustand: New. In English.

Kartoniert / Broschiert. Zustand: New. Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis.Shows how to apply a range of commonly used machine learning and deep learning techniques to biomedical problems.

Taschenbuch. Zustand: Neu. Machine Learning for Biomedical Applications | With Scikit-Learn and PyTorch | Emma C. Robinson (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Elsevier Science Publishing Co Inc | EAN 9780128229040 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Paperback. Zustand: New. Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.…