Here's a table with concrete examples illustrating when to use different types of neural networks – RNNs, LSTMs, and GRUs: | Scenario | RNN | LSTM | GRU | Explanation | |----------|-----|------|-----|-------------| | Short Sequences | ✅ | ❌ | ❌ | RNNs are efficient for short-term dependencies, making them suitable for short sequences. | | Long Sequences with Complex Dependencies | ❌ | ✅ | ✅ | LSTMs and GRUs are designed to handle long-term dependencies effectively. | | Limited Computational Resources | ❌ | ❌ | ✅ | GRUs, being simpler than LSTMs, are more computationally efficient. | | Real-time Processing | ✅ | ❌ | ✅ | RNNs and GRUs are faster, making them better for real-time applications. | | Time-Series Analysis | ✅ | ✅ | ✅ | All can be used, but LSTMs and GRUs are preferable for long-range forecasts. | | Text Generation | ❌ | ✅ | ✅ | LSTMs and GRUs excel in handling the complexity of language for text generation. | | Speech Recognition | ❌ | ✅ | ✅ | The ability to remember long-term features makes LSTMs and GRUs ideal for speech recognition. | This table provides a general guide; the best choice depends on the specific requirements and constraints of your project.