Edward discusses the architecture of a recurrent neural network designed for processing medical codes, treating them as multi-hot vectors to capture multiple diagnoses or procedures in a single encounter. He shares insights on dimensionality reduction, experimenting with different hidden layer sizes, and the challenges of predicting across a vast 30K-dimensional space, emphasizing the complexity of softmax in such a high-dimensional classification problem.
