Artificial Intelligence, Machine Learning and Deep Learning techniques are becoming more relevant on several research fields for which scientists rely on computational frameworks such as Scikit Learn, TensorFlow, PyTorch etc. which are specially designed to facilitate the development of Machine and Deep learning applications for research and commercial objectives. These frameworks provide a full set of libraries that allow researchers to become productive within a relatively small amount of time and with a minimum knowledge of traditional MPI and GPU programming (ML and DL frameworks are commonly already GPU-accelerated).
This course will focus on Machine Learning applications through the introduction of basic concepts and categories, the common workflow of application development through some simple examples, and some challenges and advantages of transitioning to HPC systems.
Prerequisites
Working knowledge of Python and Linux command line is essential. Experience with Python machine and deep learning libraries is beneficial.
Please refer to the setup section for a list of the software used for this training course.