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      Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch

      7 in stock

      Firm sale: non returnable item
      SKU 9780128229040 Categories ,
      Select Guide Rating
      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...

      £57.95

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      Description

      Product ID:9780128229040
      Product Form:Paperback / softback
      Country of Manufacture:GB
      Title:Machine Learning for Biomedical Applications
      Subtitle:With Scikit-Learn and PyTorch
      Authors:Author: Emma C. Robinson, Maria Deprez
      Page Count:304
      Subjects:Artificial intelligence, Artificial intelligence, Machine learning, Neural networks and fuzzy systems, Computer vision, Machine learning, Neural networks & fuzzy systems, Computer vision
      Description:Select Guide Rating
      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.
      Imprint Name:Academic Press Inc
      Publisher Name:Elsevier Science Publishing Co Inc
      Country of Publication:GB
      Publishing Date:2023-09-13

      Additional information

      Weight636 g
      Dimensions191 × 237 × 18 mm