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(This article was first published on <strong><a href="http://feedproxy.google.com/~r/DataRValue/~3/lT2nfkTLpfE/training-neural-networks-with.html"> Data R Value</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><span><span><span lang="en"><span>Neural networks have been a very important area of scientific study that has evolved by different disciplines such as mathematics, biology, psychology, computer science, etc.</span></span></span></span><br><span><span><span lang="en"><span>The study of neural networks leapt from theory to practice with the emergence of computers.<br>Training a neural network by adjusting the weights of the connections is computationally very expensive so its application to practical problems took until the mid-80s when a more efficient algorithm was discovered. </span></span></span></span><br><span><span><span><span lang="en"><span>That</span><span> algorithm is now known as back-propagation errors or simply backpropagation.</span></span></span></span></span><br><span><br></span><span><span><span lang="en"><span>One of the most cited articles on this algorithm is:</span></span></span></span></p>
<p><span><span><b>Learning representations by back-propagating errors</b></span></span><br><span><span><span><span>David E. Rumelhart<sup><span>*</span></sup></span><span>, Geoffrey E. Hinton<sup><span>†</span></sup></span><span> &amp; Ronald J. Williams<sup><span>*</span></sup></span></span></span></span><br><span><span><i>Nature</i> <b>323</b>, 533 – 536 (09 October 1986)</span></span></p>
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<p><span><span><span><span lang="en"><span><span lang="en"><span>Although it is a very technical article, anyone who wants to study and understand neural networks is obliged to pass through this material.</span></span> </span></span></span></span></span><br><span><br></span><span><span><span><span lang="en"><span>I share the entire article in:</span></span></span></span></span><br><span><a target="_blank" rel="nofollow" href="https://github.com/pakinja/Data-R-Value/tree/master/MachineLearning_NeuralNetworks_Articles">https://github.com/pakinja/Data-R-Value</a></span></p>
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