Download Neural Networks and Computing: Learning Algorithms and by Tommy W. S. Chow PDF
By Tommy W. S. Chow
This publication covers neural networks with distinct emphasis on complex studying methodologies and functions. It contains functional problems with weight initializations, stalling of studying, and break out from an area minima, that have no longer been coated via many latest books during this quarter. also, the e-book highlights the $64000 function choice challenge, which baffles many neural networks practitioners as a result of the problems dealing with huge datasets. It additionally includes a number of attention-grabbing IT, engineering and bioinformatics purposes.
Read Online or Download Neural Networks and Computing: Learning Algorithms and Applications (Series in Electrical and Computer Engineering) PDF
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Additional info for Neural Networks and Computing: Learning Algorithms and Applications (Series in Electrical and Computer Engineering)
The dramatic increase in learning rate may lead the network to get stuck in a local minimum in some neural network problems. This phenomenon can be avoided by the following strategy. 1, the last step is cancelled and η (t ) is reduced by half. This strategy gives a small preference to learning rate reduction and enhances the robustness of the training process. 2. , the dot product of inputs and weights, and the activation function. The weights of neural networks are firstly initialized by a random generator.
2. Evaluate the gradient of the error function with respect to each model parameter. 3. Adjust the model parameters by a certain step size in the direction of the steepest gradient. 4. Repeat steps 2 and 3 until the minimum is found. Interestingly, the linear model shown in Eq. 5) can be implemented by the simplest form of a neural network shown Fig. 16. This simple network consists of a bias neuron, an input neuron, and a linear output neuron. In most feedforward neural network implementation, the bias neuron is set to have a constant input 1.
13(b) shows the architecture of a typical SOM network, in which all neurons, arranged on a fixed grid of the output layer, contain a weight vector similar to the input dimension. After the training, each neuron becomes representative of different types of input data. One of the most important characteristic of SOM lies in is its topological ordering which means that the neurons that have similar weight (in the input dimension) also close to each other in the SOM output map. This type of SOM map is useful in a many applications including clustering, visualization, quantization and retrieval.