### Gradient Descent Algorithm for Artificial Neural Networks in Machine Learning – 17CS73

### Video Tutorial

### Gradient Descent and Delta Rule in ANN

Gradient Descent and the Delta Rule is used separate the Non-Linearly Separable data.

Weights are updated using the following rule,

Where,

### Gradient Descent Algorithm

**Gradient descent is an important general paradigm for learning.**

It is a strategy for searching through a large or infinite hypothesis space that can be applied whenever

- the hypothesis space contains continuously parameterized hypotheses (e.g., the weights in a linear unit), and
- the error can be differentiated with respect to these hypothesis parameters.

**The key practical difficulties in applying gradient descent are**

- Converging to a local minimum can sometimes be quite slow (i.e., it can require many thousands of gradient descent steps), and
- If there are multiple local minima in the error surface, then there is no guarantee that the procedure will find the global minimum.

## Summary

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