Deep learning projects


This page is intended as a tutorial for students in my research group to begin understanding deep learning. It contains a series of simulated problems that help demonstrate concepts. It links to tutorials from some other sites to provide real world examples. Finally, it ends with links to our data sets and problems my group is currently researching.

Software options and installation

There are many software packages that can be used to implement neural networks (NNs) for deep learning. This article compares the most popular as of 2019. We use TensorFlow. To install it on your computer, follow these instructions, which includes the step of installing Python. Be careful about versions. Python, pip and TensorFlow are being updated often. You will probably not want to run the very latest because they may not be compatible with each other. If you run into this problem, back up a few versions.

The Clemson CCIT group offers a series of training classes on python programming, big data, and machine learning. They also operate the Palmetto high performance computing cluster. Students can request an account on this platform to speed up computations or run them remotely.

Simulations

The strength of deep learning is that it can be applied to complex classification problems, such as recognizing pictures of different types of animals (e.g. dogs vs cats). However, starting to study deep learning by working on these problems can be overwhelming. The purpose of these simulations is to enable students to study deep learning concepts on simple problems. Each problem has a specific known feature or challenge so that we can see how deep learning is identifying that feature.

Tutorials from other sources

Our data sets

In a set of projects we are using deep learning to study classification of datasets our group has collected over the years:

Helpful study sites


Deep learning projects page / ahoover@clemson.edu