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

De (autor) ,
Notă GoodReads:
en Limba Engleză Paperback – September 2023

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.

 

 

 

  • 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 in biomedical problems
  • Develops practical computational skills that are needed to manipulate complex biomedical data sets
  • Shows how to design machine learning experiments that address specific problems related to biomedical data

 

 

 

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Specificații

ISBN-13: 9780128229040
ISBN-10: 0128229047
Pagini: 326
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

Part I: Introduction and background
1. Machine learning for Biomedical applications
2. Programming background
3. Mathematical background

Part II: Machine Learning Methods
4. Regression
5. Classification
6. Ensemble methods
7. Dimensionality reduction and Manifold learning
8. Feature extraction and selection
9. Clustering 10. Neural networks

Part III: Deep Learning
11. Building blocks of deep neural networks
12. Common architectures
13. Generative models
14. The challenges of working with biomedical data

Part IV: Tricks of the trade