Editing Neural network (machine learning) (section)

==Training==
Neural networks are typically trained through [[empirical risk minimization]]. This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset.<ref name=":2">{{Cite book |last1=Vapnik |first1=Vladimir N. |title=The nature of statistical learning theory |last2=Vapnik |first2=Vladimir Naumovich |date=1998 |publisher=Springer |isbn=978-0-387-94559-0 |edition=Corrected 2nd print. |location=New York Berlin Heidelberg}}</ref> Gradient-based methods such as [[backpropagation]] are usually used to estimate the parameters of the network.<ref name=":2" /> During the training phase, ANNs learn from [[Labeled data|labeled]] training data by iteratively updating their parameters to minimize a defined [[Loss functions for classification|loss function]].<ref name=":4">{{cite book |author=Ian Goodfellow and Yoshua Bengio and Aaron Courville |url=http://www.deeplearningbook.org/ |title=Deep Learning |publisher=MIT Press |year=2016 |access-date=1 June 2016 |archive-url=https://web.archive.org/web/20160416111010/http://www.deeplearningbook.org/ |archive-date=16 April 2016 |url-status=live}}</ref> This method allows the network to generalize to unseen data.{{multiple image
 | direction = horizontal
 | total_width = 400
 | footer    = 
 | image1    = Simplified neural network training example.svg
 | alt1      = 
 | caption1  = Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict [[starfish]] and [[sea urchin]]s, which are correlated with "nodes" that represent visual [[Feature (computer vision)|features]]. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.
 | image2    = Simplified neural network example.svg
 | alt2      = 
 | caption2  = Subsequent run of the network on an input image (left):<ref>{{cite book |author=Ferrie, C. |author2=Kaiser, S. |year=2019|title=Neural Networks for Babies|publisher=Sourcebooks|isbn=978-1-4926-7120-6}}</ref> The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a [[false positive]] result for sea urchin.<br />In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.}}