Deep Learning - Recurrent Neural Networks with TensorFlow - GRU and LSTM (Part 2)

The video tutorial covers the differences between LSTM and GRU, highlighting their structures, gates, and performance based on recent research. It emphasizes the importance of experimentation over philosophical reasoning in machine learning. The LSTM is explained in detail, including its gates and states, and practical implementation tips are provided for using LSTM in TensorFlow and Keras.

Why is it suggested to rely on experimental results rather than philosophical reasoning when choosing between LSTM and GRU?

What are the advantages of using LSTM over GRU according to recent research?

How can one implement LSTM or GRU in a model?

What are the implications of using different activation functions in LSTM?