Recent advances in deep neural networks coupled with an increasing amount and complexity of scientific data collected in a wide array of domains provide many exciting opportunities for deep learning applications in scientific settings . Furthermore, libraries such as Google’s Tensorflow and the higher-level Keras library have made building deep learning models more accessible through common tools freely available to researchers, including R.
In this course, we’ll introduce some basic neural network and deep learning theory and give participants practical experience in some popular deep learning models and techniques.
Content includes:
- Introduction to Regression and Classification using neural networks
- Image classification with Convolutional neural networks
- Speeding up the training process of your own models using existing neural network
Course Delivery
There will be a significant practical element to the course and we will be working online in Google Colab notebooks.
Course Requirements
Familiarity with R programming basics.
