- Perform pixel-level semantic segmentation on images
- Import and use pre-trained models from TensorFlow and Caffe
- Speed up network training with parallel computing on a cluster
- Use data augmentation to increase the accuracy of a deep learning model
- Automatically convert a model to CUDA to run on GPUs
About the Presenters
Abhijit Bhattacharjee is a Senior Application Engineer at MathWorks, specializing in the areas of computer vision, audio signal processing, and machine learning. Prior to MathWorks, Abhijit was a researcher at USC Information Sciences Institute, working in programs funded by NASA and DARPA. Projects included hyperspectral image processing and audio steganography. He holds an M.S.E.E. degree from the University of Southern California and works with clients in all industries, including consumer devices, semiconductors, government, and academic.
Pitambar Dayal is a Technical Marketing Manager for MathWorks Image Processing and Computer Vision products. Prior to MathWorks, Pitambar earned his B.S. studying Biomedical Engineering at NJIT and working in a brain-imaging lab, where he researched fMRI patterns in ischemic stroke patients (using MATLAB, of course). Outside of work, Pitambar spends his time traveling, watching basketball, and playing ultimate frisbee. His favorite food is Margherita Pizza and his favorite dessert is Belgian waffles.