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