This week, I primarily focused on implementing the script that will train an NN architecture and save the model for future inference use. This implementation has a variety of features including variable choices of input data, cross-validation raining to identify the optimal stepsize, and usage of the wandb package to track training performance through the epochs. I have also implemented the SVM model trainer. This was pretty straightforward with a heavy dependence on the sklearn package. I also tuned up the data collection for an automated collection of 26 letters in one shot. I performed some research into pre-training data analysis based on my “Estimation, Detection, and Learning” class. Based on the research, I added some potential action items to prepare a clean dataset.
I am on schedule as of right now.
Next week, I hope to put significant work into the design document. In addition, I hope to flesh out a robust testing module, finalize dataset analysis, and begin collecting data with the glove (dependent on the glove’s progress).