By Kevin L. Priddy
This educational textual content offers the reader with an knowing of man-made neural networks (ANNs) and their program, starting with the organic platforms which galvanized them, during the studying tools which were constructed and the knowledge assortment approaches, to the various methods ANNs are getting used today.
The fabric is gifted with not less than math (although the mathematical info are incorporated within the appendices for readers), and with a greatest of hands-on event. All really good phrases are incorporated in a word list. the result's a hugely readable textual content that might educate the engineer the guiding ideas essential to use and practice synthetic neural networks.
- studying Methods
- information Normalization
- info assortment, coaching, Labeling, and enter Coding
- Output Coding
- Supervised education Methods
- Unsupervised education Methods
- Recurrent Neural Networks
- A Plethora of Applications
- facing constrained quantities of Data
- Appendix A: The Feedforward Neural Network
- Appendix B: function Saliency
- Appendix C: Matlab Code for varied Neural Networks
- Appendix D: thesaurus of Terms
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Additional info for Artificial Neural Networks: An Introduction
Each stock in the index is selected for liquidity, size, and industry and is weighted for market capitalization. Using the S&P 500 feature rather than each of the individual stocks to track the market is a way to reduce the total number of individual stocks that need to be tracked, while retaining important trend information. Thus, the S&P 500 index is an extracted feature that uses domain-specific knowledge. For some applications, an extracted feature can provide all of the information the neural network needs.
3 Training and Testing Once the network designer has defined the problem, chosen the neural network architecture, and collected, prepared, and labeled the data, he is ready to train the neural network. The training goal is to find the training parameters that result in the best performance, as judged by the neural network’s performance with unfamiliar data. This determines how well the network will generalize. Generalization is a measure of how well the classifier performs on data samples with which it has never been presented, but that are within the acceptable limits of the input feature space.
After training, the neural network was presented with the test data. The network worked remarkably well at discriminating between images with tanks and those without. S. Army was pleased with the results, officials were suspicious of the neural network’s success. A second data-collection round was commissioned and the network re-tested. This time, the neural network performed poorly. After a lengthy examination, it was determined that all the original images of tanks had been collected on a cloudy day and all the images without tanks were collected on a sunny day.
Artificial Neural Networks: An Introduction by Kevin L. Priddy