<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><br><br><span><span>Learning representations by back-propagating errors</span></span><br><span><span><span><span>David E. Rumelhart<span>*</span></span><span>, Geoffrey E. Hinton<span>†</span></span><span> &amp; Ronald J. Williams<span>*</span></span></span></span></span><br><span><span><i>Nature</i> 323, 533 - 536 (09 October 1986)</span></span><br><br><div><a href="https://3.bp.blogspot.com/-ceBrRsuq1NE/WRyA7xTLB8I/AAAAAAAADCQ/1OZ-VKwGvD0sd7VT_1tfw2FGNG4Junm5ACLcB/s1600/back.png"></a></div><br><div></div><br><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" href="https://github.com/pakinja/Data-R-Value/tree/master/MachineLearning_NeuralNetworks_Articles">https://github.com/pakinja/Data-R-Value</a></span><br><br>