Using neural networks and deep learning, medical research has begun to show how common processes within medicine can be automated and operationalized. Join us for a brief introduction to neural networks, with an emphasis on the many modular building blocks that make this learning method particularly powerful. Once we've covered the basics, we'll use the python library Keras to build our own neural network. Then, we'll train our network on breast cancer data, see how to make predictions on unseen data, and discuss practical considerations for building effective networks.

Before the lesson: we will be hosting our code through Kaggle, which has a free online python editor. Please make an account on Kaggle before the lesson so that you can access the materials! Our mini-series video on Kaggle has a quick primer on Kaggle's functionality, for those who are interested. If you have no prior experience with python, we heavily recommend watching through our introduction to python video before the workshop.

Head to our GitHub site and navigate to "Workshop 5" to access the workshop code, recording, and presentation slides.