Difference Between ReNNs and RNN
Features |
Recursive Neural Network |
Recurrent Neural Network |
---|---|---|
Architecture |
Network having Hierarchical structure, Tree-like structure. |
Chain-like structure known as Sequential structure. |
Data Processing |
It processes hierarchical data. |
It processes sequential and time-series data. |
Memory Handling |
Limited context handling. |
Captures context through sequential memory. |
Connections |
Connections are based on hierarchical structure. |
Connections are based on sequential order. |
Training Complexity |
This network requires specific tree traversal algorithms for training. |
It involves training backpropagation through time, |
Dependency Understanding |
Explicitly models dependencies in a tree structure. |
Implicitly captures dependencies in sequences. |
Use cases |
Image parsing, document structure analysis. |
Language modeling, speech recognition |
By understanding the differences between these two network architectures helps in choosing the appropriate neural network for specific tasks. Recursive Neural Networks are suitable for tasks involving hierarchical structures, while Recurrent Neural Networks excel in capturing sequential dependencies.
Difference between Recursive and Recurrent Neural Network
Recursive Neural Networks (RvNNs) and Recurrent Neural Networks (RNNs) are used for processing sequential data, yet they diverge in their structural approach. Let’s understand the difference between this architecture in detail.