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ANNs2

There are many forms of neural networks. Most operate by passing neural ‘activations’ through a network of connected neurons.

One of the most powerful features of neural networks is their ability to learn and generalize from a set of training data. They adapt the strengths/weights of the connections between neurons so that the final output activations are correct.

There are three broad types of learning:

1. Supervised Learning (i.e. learning with a teacher)

2. Reinforcement learning (i.e. learning with limited feedback)

3. Unsupervised learning (i.e. learning with no help)

This module will study in some detail the most common learning algorithms for the most common types of neural network.

1-Supervised Learning

- A teacher is present during learning process and presents expected

   output.

- Every input pattern is used to train the network.

- Learning process is based on comparison, between network s computed  

   output and the correct expected output, generating "error".

- The "error" generated is used to change network parameters that result   

   improved performance.

2- Unsupervised Learning

- No teacher is present.

- The expected or desired output is not presented to the network.

- The system learns of it own by discovering and adapting to the structural features in the input patterns.

3- Reinforced learning

- A teacher is present but does not present the expected or desired output but only indicated if the computed output is correct or incorrect.

- The information provided helps the network in its learning process.

- A reward is given for correct answer computed and a penalty for a wrong answer.

Note : The Supervised and Unsupervised learning methods are most popular forms of learning compared to Reinforced learning.