Deep Learning in Depth: The Good, the Bad, and the Future
“As you compose more and more non-linear functions together, you can represent a much wider function space than you could with just one non-linear function. That is why deep learning is different from shallow learning. Shallow learning doesn’t compose multiple things together. Deep learning does.”
Although traditional machine learning methods are being successfully used to solve many problems in cybersecurity, their success often depends on choosing and extracting the right features from a data set, which can be hard with complex data. In this podcast, Ritwik Gupta and Carson Sestili explore deep learning, a popular and quickly growing subfield of machine learning that has had great success on problems about these data sets, and on many other problems where picking the right features for the job is hard or impossible.
About the Speaker
Ritwik Gupta is a machine learning researcher at the Carnegie Mellon University Software Engineering Institute’s Emerging Technology Center. His research focuses on the intersection of machine learning and health, with many forays into the areas of robotics, adversarial learning, and computational linguistics. He is passionate about educating people about machine learning and the many cool and unique ways it can be applied to unorthodox problem domains.
Carson Sestili is a machine learning research scientist in the CERT Data Science group, where he uses data science, statistics, and machine learning for research in cybersecurity and intelligence. His work at CERT has involved applying machine learning for problems in satellite image recognition, code security defect detection, and cyber-incident forecasting. He has also investigated machine learning models that identify novel and ambiguous data.