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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.
This tutorial discusses the Gradient Descent Algorithm in Machine Learning. If you like the tutorial share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.