Recurrent Neural Networks (RNNs)
RNNs handle sequential data such as text, speech, and time series. It explains recurrent connections, long short-term memory (LSTM) networks, and gated recurrent units (GRU). Learners examine how sequence modeling captures temporal dependencies. Applications include translation, sentiment analysis, and speech recognition. It highlights strengths and limitations of recurrent architectures. RNNs form the basis for many natural language processing systems.
Sequence Modeling Focus:
- Temporal dependency modelling
- Advanced recurrent architectures
- Language and speech applications
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