I went with the spatial vs sequential angle, CNNs are built for grid-like data where local patterns matter, RNNs are for sequences where order and context carry meaning.
Start by defining CNNs and RNNs in one sentence each, then contrast them across key dimensions like data type, architecture, parameter sharing, and use cases. Emphasize that the choice depends on the problem structure: spatial vs. sequential data.
Pro tip: Mention that CNNs can be used for sequential data (e.g., WaveNet) and RNNs for spatial data (e.g., image captioning), showing you understand that the boundaries are not rigid and that hybrid models exist.
Briefly define CNN as designed for grid-like data (e.g., images) using convolution and pooling, and RNN as designed for sequential data (e.g., time series) using recurrent connections with memory.
Highlight that CNNs assume spatial locality and translation invariance, while RNNs assume temporal dependencies and variable-length sequences.
Discuss differences in parameter sharing (CNNs share weights across spatial locations, RNNs share across time steps), depth (CNNs are feedforward, RNNs have loops), and memory (RNNs have hidden state, CNNs do not).
Mention that CNNs are easier to parallelize and less prone to vanishing gradients, while RNNs suffer from vanishing/exploding gradients and are harder to parallelize due to sequential dependencies.
Conclude with typical applications: CNNs for image classification, object detection; RNNs for language modeling, speech recognition. Note that transformers are increasingly replacing RNNs for sequences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.