Machine Learning for Biomedical Applications
With Scikit-Learn and PyTorch
- Publisher's listprice EUR 65.95
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27 975 Ft (26 643 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 5 595 Ft off)
- Discounted price 22 380 Ft (21 314 Ft + 5% VAT)
27 975 Ft
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Academic Press
- Date of Publication 13 September 2023
- ISBN 9780128229040
- Binding Paperback
- No. of pages304 pages
- Size 234x190 mm
- Weight 1000 g
- Language English
- Illustrations 84 illustrations (48 in full color) 537
Categories
Long description:
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.
Table of Contents:
1. Programming in Python
2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch
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