This half-day lesson gives an introduction to artificial neural networks. During the lesson we prepare and visualise data for algorithm development, and construct a neural network that is able to classify disease in X-rays.
It is the third lesson in the machine learning curriculum. In the final lesson we explore the important area of responsible machine learning.
- Introduction to Machine Learning in Python [Lesson materials; Code repository]
- Introduction to Tree Models in Python [Lesson materials; Code repository]
- Introduction to artificial neural networks in Python [Lesson materials; Code repository]
- Responsible machine learning in Python [Lesson materials; Code repository]
These lessons are being run at University of Edinburgh as part of the Ed-DaSH Data Science training programme for Health and Biosciences.
The first lessons were taught in May: https://edcarp.github.io/2022-05-24_ed-dash_machine-learning/. For a list of future lessons, see: https://edcarp.github.io/Ed-DaSH/workshops
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Please see the current list of issues for ideas for contributing to this
repository. For making your contribution, we use the GitHub flow, which is
nicely explained in the chapter Contributing to a Project in Pro Git
by Scott Chacon.
Look for the tag 
Current maintainers of this lesson are:
A list of contributors to the lesson can be found in AUTHORS
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