You have a pretrained hybrid model composed of a CNN followed by a 3-layer RNN that was trained to classify a different set of objects. To adapt it to a new but related classification task with the least effort, which actions should you take? (Choose two.)

When adapting a pretrained model to a new, related task, the goal is to leverage the learned features while fine-tuning for the specific new task. Reinitializing the weights of the CNN’s final fully connected layer (or the RNN's final layer) and retraining allows the model to learn the new classification boundaries while retaining the powerful feature extraction capabilities of the earlier layers. This is a common transfer learning technique. Reinitializing all weights in the CNN or the entire network would discard the valuable learned features, essentially starting training from scratch, which is inefficient and negates the benefit of pretraining. Retraining only the CNN after reinitializing its final layer is appropriate if the RNN part of the hybrid model is not directly involved in the final classification layer that needs adaptation. Retraining the full model after reinitializing the RNN's final layer is also valid as it allows the RNN to adapt its sequence processing for the new task while leveraging the CNN's learned features.