Introduction to Deep Learning for Biologists
Dates: Online, September 30 - October 4, 2024
Course website: https://www.physalia-courses.org/courses-workshops/course67/
Course Overview:
Are you a biologist curious about deep learning and how it can be applied to your research? Join us for a comprehensive introduction to deep learning predictive algorithms, specifically tailored for biological data. This course will delve into the fundamentals of deep learning, with a focus on Convolutional Neural Networks (CNNs) for classification, regression, and image segmentation tasks. We'll also cover critical topics like statistical learning, cross-validation, overfitting, and model generalizability.
Course Format:
The course is structured into modules spread over five days. Each day will feature a mix of lectures, class discussions, and practical hands-on sessions. You'll engage in collaborative exercises, applying the skills you've learned with the support of instructors. These exercises will help you interpret and discuss the results in real-time, enhancing your understanding of deep learning concepts.
Target Audience:
This course is designed for advanced students, researchers, and professionals in biology who are eager to learn about deep learning and how to apply it to their research. Whether you're a beginner or have some experience with deep learning, this course will provide valuable insights. We'll start with an introduction to deep learning concepts and progressively dive deeper into the mechanics of model development.
Prerequisites:
Participants should have a background in biology and familiarity with research problems involving prediction, inference, or pattern discovery. While prior experience with predictive experiments is beneficial, it's not required. Basic knowledge of Python and Linux will be helpful, but we welcome all learners. For those looking to brush up on Python before the course, we recommend checking out our preparatory exercises on GitHub: Python Coding Exercises.
Learning Outcomes:
By the end of this course, you will have gained:
- A solid understanding of the theoretical foundations of deep learning and its core building blocks.
- The ability to distinguish between classification, regression, and segmentation, and to apply these concepts to biological data.
- Experience in building deep learning models, evaluating their performance, and selecting the most appropriate model for your research.
- Practical skills in preparing and augmenting real-world data for statistical learning.
Don't miss this opportunity to enhance your deep learning skills and apply them to your biological research!
