Machine Learning for Biomedical Applications | With Scikit-Learn and PyTorch
Language: English
Published by Elsevier Science Publishing Co Inc, 2023
- Softcover
- New
Seller: preigu, Osnabrück, Germanypreigu
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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.
Seller Inventory # 119651386
- Title
- Machine Learning for Biomedical Applications | With Scikit-Learn and PyTorch
- Author
- Emma C. Robinson (u. a.)
- Publisher
- Elsevier Science Publishing Co Inc
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0128229047
- ISBN 13
- 9780128229040
- Item weight
- 636 grams
- Dimensions
- 191 x 237 x 18 mm
- Seller catalogs
- Bücher
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 to biomedical problems.
- Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets.
- Shows how to design machine learning experiments that address specific problems related to biomedical data
"Synopsis" may belong to another edition of this title.
About the Author
Dr Robinson's research focuses on the development of computational methods for brain imaging analysis, and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex “ (Glasser et al, Nature 2016), and has featured as a central tenet in the HCP’s paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples.
"About the title" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 60 to 60 business days | 60 to 60 business days |
|---|---|---|
| First item | £ 60.18 | £ 60.18 |
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