# Sequence Model — Week 01

*Different Types of RNN*

Language Modelling → Cost Function

Exploding Gradients are easy to capture as parameters just blow up and you might often see NaNs (Not a numbers → results of numerical overflow, in Neural network computation) → Apply Gradient Clipping i.e. Look at Gradient Vectors and if it is bigger than some threshold, re-scale some of your gradient vector so that is not too big.

**Bi-directional RNN**

*LSTM*

**Peephole Connections** → Gate Values may depend not just on a_t-1 and x_t but also on previous memory cell value

**QUIZ**

**Notebook Exercise**

*EXERCISE →**02*