Our "Introduction to Machine Learning with Python" workshop is a free event open to all FSU students, faculty, and staff. The workshop aims to introduce the basics of machine learning using the Python programming language. We will focus on deep learning via a convolution neural network. Next, we will present examples using cutting-edge software tools like Google's TensorFlow.
Training a deep neural network can be very CPU-intensive. In this workshop, we will cover the basics of accelerating learning process via hardware accelerators such as the Graphics Processing Units (GPUs).
- basics of deep learning neural network
- introduction to TensorFlow
- accelerating deep learning via GPUs
How to Prepare
Familiarity with the Python programming language is recommended but not required. No prior experience with GPUs or TensorFlow is expected.
This is an interactive, hands-on workshop. Attendees are encouraged to bring a laptop computer if you are able to so that you can follow along during the presentation.
Before the workshop begins, be sure that you have installed an SSH client on your computer. If you have a Mac or Linux computer, an SSH client is already built into the operating system. If you have a Windows computer, refer to our instructions for how to setup an SSH client.
If you want, you can sign-up for an RCC account on our website. However, this is not necessary. If you do not have an account already, we will provide you a temporary account to use on our system during the workshop.
This workshop will be led by Bin Chen, PhD, an Applications Specialist at RCC. Dr. Chen has many years of experience in high performance computing, scientific Python development, and other related technologies.
This workshop will occur in Dirac Science Library Room 499 in the Department of Scientific Computing. Enter the main entrance of Dirac Science Library, and immediately turn left before entering the library. Take the elvator to the 4th floor. Once on the 4th floor, walk straight ahead until you reach the large Seminar Room (499).