Deep Learning · TEE · section
Sequence mappings and applications
Recurrent neural networks
Definition. A sequence-model configuration specifies how many inputs and outputs are processed and how their time indices relate. One-to-many, many-to-one and many-to-many configurations correspond to different prediction tasks.
Explanation and working
| Mapping | Meaning | Example and output |
|---|---|---|
| One to many | One input conditions several outputs | Image captioning: image to a word sequence |
| Many to one | Sequence summarized as one output | Review sentiment: words to positive neutral or negative |
| Many to many aligned | Output associated with each input time | Video frame labeling or per-word tagging |
| Many to many unaligned | Output sequence length may differ | Encoder-decoder translation |
Music generation can use earlier notes to predict the next note repeatedly. Next-word prediction uses the prefix to produce a probability distribution over a vocabulary. A word with the largest predicted probability is a simple choice, but the precise prediction cannot be calculated without the learned model's weights.
Sentence explanation: In “The dogs that bark near my house are loud,” the model should retain the plural subject across the intervening words when choosing “are”. Gates can retain subject information and admit later context. This is an interpretation of what a trained model may learn, not a claim that an individual gate is guaranteed to represent grammar.
Sentiment model answer: tokenize the review, map tokens to numerical embeddings, process them with an RNN/LSTM/GRU, use a final state or pooled sequence representation, then a dense layer and softmax over three sentiment classes. Train with categorical cross-entropy on labeled reviews; evaluate on held-out data. A bidirectional model is not needed for this answer and is not explicitly listed in the current syllabus.