Advanced Techniques in LSTM Models
Learn to enhance LSTM models for predicting sequential data by exploring unrestricted, bidirectional, and recurrent dropout methods. Understand data temporalization, scaling, and the impact of time windows on LSTM performance. Gain insights into best practices and challenges in advanced LSTM modeling.
We'll cover the following...
We'll cover the following...
LSTM models worked better than MLPs, which was expected because they can learn temporal patterns. The baseline restricted LSTM model beat the best MLP model in the previous chapter. The unrestricted LSTM proved to perform even better. Adding a recurrent dropout for regularization further improved and ...