Practical Deep Learning for Images, Sensor and Text
Practical Deep Learning for Images, Sensor and Text


Deep Learning became known for beating human performance on image classification, and most applications and published examples for deep learning still focus on image processing. However, deep learning nowadays can also be applied successfully to time-series (or sensor) data and text. In this hands-on MATLAB workshop, you will see how easy it is to get started applying deep learning to various data types, including images, text, and time-series data. You’ll use an online MATLAB instance to perform the following tasks:
1. Train deep neural networks on GPUs in the cloud
2. Access and explore pretrained models
3. Build a CNN to solve an image classification problem
4. Use LSTM networks to solve a time-series and text analytics problem.


Renee is an Application Engineer supporting the Medical Devices Industry in Data Analytics and Technical Computing applications.  She works closely with engineers and researchers in the biomedical community to understand and address the unique challenges and needs in this industry.  Renee graduated Northwestern University with an M.S. in Biomedical Engineering.  Her research was in medical imaging focusing on quantitative cerebrovascular perfusion MRI of the brain for stroke prevention.  She joined the MathWorks in 2012 helping customers with MATLAB, analysis, and graphics challenges, and later transferred to Application Engineering where she specialized in Test and Measurement applications before transitioning to her current role.