8 Questions
What happens when the entries of the output gate in LSTM approach 1?
All memory information is passed through to the predictor
In what scenarios is bidirectional RNN useful?
For signal smoothing and denoising scenarios
What are the trainable parameters in LSTM networks?
Weight matrices and bias vectors
How do bidirectional RNNs differ from traditional RNNs?
Bidirectional RNNs combine causal and anti-causal operations
What does an output gate close to 0 indicate in LSTM?
No further processing is done and information is retained within the memory cell
For what type of systems are RNNs primarily designed?
Causal systems with inference based on observed inputs until a given time instance
How do bidirectional RNNs leverage past and future observations?
By combining past and future operations through distinct RNNs
What differentiates bidirectional RNNs from traditional RNNs in terms of operation?
Bidirectional RNNs combine causal and anti-causal operations
Explore the vanilla implementation of a Recurrent Neural Network (RNN) with a single hidden layer model as illustrated in Fig 2(b). Learn about the mapping of variables, updating hidden states, and generating outputs using fully-connected layers. Understand the main challenge of optimizing RNNs due to the presence of loops.
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