Module- 4 --RNN

1. Representation of an RNN expanded across multiple time steps
3. A single point or step in a sequence processed by an RNN
5. Recurrent neural network architecture designed to handle long-term dependencies
8. Direction in which a bidirectional RNN processes a sequence from end to beginning
9. Problem in which gradients become extremely small during training
14. Describes a neural network that uses information from previous time steps
16. The internal representation maintained by an RNN at each time step
17. Direction in which an RNN processes a sequence from beginning to end
19. Relationship between elements of a sequence that may occur across different time steps
20. A modified form of BPTT that backpropagates through only a limited number of time steps
21. RNN that processes a sequence in both forward and backward directions
22. Information maintained by an RNN from previous time steps
23. Neural network designed to process sequential or time-dependent data

2. A weakness or drawback of a standard RNN
4. Structure used to represent computations and dependencies in an RNN
6. Describes dependencies between events separated by many time steps
7. Algorithm used to calculate gradients for updating neural-network weights
10. An ordered set of data elements where the order of elements is important
11. Method used to train an RNN by propagating errors backward through time
12. Mathematical quantity used to determine how weights should be updated
13. Relating to the graph that represents mathematical operations in a neural network
15. Problem in which gradients become extremely large during training
18. Basic RNN architecture without advanced memory mechanisms such as LSTM gates
20. Abbreviation for Truncated Backpropagation Through Time