Training Algorithms

Training Algorithms

Every neural network model has four parameters:

1. The number of hidden layers and nodes

2 and 3. The type of activation function from input to hidden and from hidden to output layer

4. The weights of the connections that send the learning signal from one node to another.

Trial and error often calculated these parameters using various search algorithms and a fitness function for assessment.

Training algorithms, sometimes referred to as learning rules, are search algorithms that are used to determine the ideal weights for each network link. Although there are many other algorithms, the most popular training algorithms are covered here.

Out of them, this video explains the :

Conjugate Gradient

Newton method;

Quasi-Newton

Levenberg Marquadert.

The most popular and traditional methods for weight adjustments are these four approaches.

On purchase of this tutorial, you will receive the complete lecture on Ant Colony Optimization Techniques Techniques.

In addition to this Project Ideas on applying Ant Colony Optimization(ACO) and some numerical problems for practising the methods are also included.