ARTIFICIAL INTELLIGENCE FOR COMPUTING AND ITS APPLICATIONS IN THE FIELD OF NATURAL RESOURCES AND ENVIRONMENT\nLesson 3. Single-layer neural networks and network training.<\/p>\n\n\n\n
B\u00e0i s\u1ed1 3. M\u1ea1ng n\u01a1 ron m\u1ed9t l\u1edbp v\u00e0 hu\u1ea5n luy\u1ec7n m\u1ea1ng<\/strong> T\u00f3m t\u1eaft:<\/em><\/strong>
TS Nguy\u1ec5n D\u01b0 Khang<\/strong><\/p>\n\n\n\n
Summary:\n\tIn the previous article, the author presented some core functions and parameters used in artificial neural networks. In this article, the author will present single-layer artificial neural networks and network training algorithms, along with specific examples based on the functions and parameters introduced. Single-layer neural networks, being simple networks, are usually only applied when classifying objects with clearly distinct characteristics. However, this knowledge is important for readers to approach multi-layer artificial neural networks, which will be presented in later articles, serving specific applications that each interested reader can apply to their own professional field.\nKeywords: Perceptron, delta, gradient .\u00a0<\/em>
T\u1eeb kho\u00e1: <\/em><\/strong>Perceptron, delta, gradient .<\/em><\/p>\n\n\n\n