Neural Network Algorithms
Top Neural Network Algorithms
Learning of neural network takes place on the basis of a sample of the population under study. These algorithms are used to train the Artificial Neural Network. During the course of learning, compare the value delivered by output unit with actual value. After that adjust the weights of all units so to improve the prediction.
Let us see introduction of Machine Learning.
There are many Neural Network Algorithms are available for training Artificial Neural Network. Let us now see some important Algorithms for training Neural Networks:
- Gradient Descent – Used to find the local minimum of a function.
- Evolutionary Algorithms – Based on the concept of natural selection or survival of the fittest in Biology.
- Genetic Algorithm – Enable the most appropriate rules for the solution of a problem and select it. So, that they send their ‘genetic material’ to ‘child’ rules. We will learn about them in details below.
Get the introduction of learning rules in Neural Network for more understanding of Neural Network Algorithms.
Gradient Descent
We use the gradient descent algorithm to find the local smallest of a function. This algorithm converges to the local smallest. By approaching proportional to the negative of the gradient of the function. To find local maxima, take the steps proportional to the positive gradient of the function. This is gradient ascendant process.
In linear models, error surface is well defined and well known mathematical object in shape of a parabola. Then find the least point by calculation. Unlike linear models, neural networks are complex nonlinear models. Here, the error surface has an irregular layout, crisscrossed with hills, valleys, plateau, and deep ravines. To find the least point on this surface, for which no maps are available, the user must explore it.
Learn more about Artificial Neural Network Model
In this algorithm, you move over the error surface by following the line with the greatest slope. It also offers the possibility of reaching the lowest possible point. You then have to work out at the optimal rate at which you should travel down the slope.
The correct speed is proportional to the slope of the surface and the learning rate. Learning rate controls the extent of modification of the weights during the learning process.
Hence, the moment of a neural network can affect the performance of multilayer perceptron.
Evolutionary Algorithms
This algorithm based on the concept of natural selection or survival of the fittest in Biology. Concept of natural selection states that – for a given population, environment conditions use a pressure that results in the rise of the fittest in that population.
To measure fittest in a given population, you can apply a function as an abstract measure.
In the context of evolutionary algorithms, refer recombination to as an operator. Then apply it to two or more candidates known as parents, and result in one of more new candidates known as children. Apply the mutation on a single candidate and results in a new candidate. By applying recombination and mutation, we can get a set of new candidates to place in the next generation based on their fittest measure.
Read more about Future of Machine Learning
The two basic elements of evolutionary algorithms are:
- Variation operators (recombination and mutation)
- Selection process (selection of the fittest)
The common features of evolutionary algorithms are:
Evolutionary algorithms are population based.
Evolutionary algorithms use recombination mix candidates of a population and create new candidates.
On random selection evolutionary algorithm based.
Hence, on the basis of details and applied problems, we use various formats of evolutionary algorithms.
Some common evolutionary algorithms are:
Genetic Algorithm Genetic Algorithm – It provides the solution for optimization problems. It provides the solution by the help of natural evolution processes. Like mutation, recombination, crossover, and inheritance.
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