A Practical Approach to Timeseries Forecasting Using Python - Stacked LSTM Forecasting

The video tutorial explains how to implement and analyze stacked LSTMs in neural networks. It begins with setting up LSTMs with return_sequences and moves on to building a stacked LSTM model. The tutorial compares the performance of single and stacked LSTMs, highlighting the benefits of using more LSTMs for larger datasets. It also discusses potential overfitting issues with too many LSTMs. Finally, the video introduces Bi-LSTMs as the next topic.

What is the significance of setting return_sequences to true in the first LSTM layer?

What role does the activation function play in the performance of LSTMs?

Why is it important to observe the training and validation loss during model training?

How does the performance of stacked LSTMs compare to single LSTMs?

What are the potential drawbacks of adding too many LSTMs in a model?