"INTRODUCTION TO DEEP LEARNING FOR BIOLOGISTS", which will be held online from the 28th of September to the 1st of October.
The course is aimed at advanced students, researchers and professionals interested in learning what deep learning is and how to develop a deep learning model for applications in biology. It will include information useful for both absolute beginners and more advanced users willing to delve into some aspects of the implementation of deep learning. We will start by introducing general concepts of deep learning presenting a functioning model and then we will progressively describe the main building blocks of a deep learning model and how the internal machinery works. Attendees are expected to have a background in biology and the research problems involving prediction, inference, pattern discovery; previous exposure to predictive experiments would be beneficial. There will be a mix of lectures and hands-on practical exercises using mainly Python, Jupyter Notebooks and the Linux command line. Some basic understanding of Python programming and the Linux environment will be advantageous, but is not required.
Learning outcomes
At the end of the course the student will have an understanding of:
- the basic theoretical background of deep learning, both in terms of basic building blocks and of commonly used, state-of-the-art architectures
- differences between classification, regression, segmentation, and how to frame a real-world problem in terms of these classes
- the main steps involved in building a deep learning model for prediction problems in biology, comprising how to evaluate prediction accuracy and how to compare and choose different models
- how to use real-world data for statistical learning, comprising data preparation and data augmentation
Programme: https://www.physalia-courses.org/courses-workshops/course67/curriculum67/
Our other online courses: https://www.physalia-courses.org/courses-workshops/
