RT: @Ronald_vanLoon#MachineLearning Summarized in One Picture by @granvilledsc @DataScienceCtrl @analyticbridge https://t.co/gxGebwwKr2 #Fintech #DataScience #ML #BigData #Analytics #Insurtech #WealthTech #CrowdLending@fintechrockers @cashare @XMLdation @UrsBolt @News_ZV pic.twitter.com/WNqokLmpVa
— Oscar Neira (@NeiraOsci) May 20, 2018
Sunday, May 20, 2018
About Machine Learning..
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Most Machine Learning Projects for Final Year models use Stochastic Gradient Descent (SGD). This means the model doesn't look at all the data at once; it looks at small "batches" in a specific order to update its "weights" (the internal connections that determine its decisions). The "Re-ordering Attack" works because the gradients (the directions the model takes to improve) are calculated based on the current batch. By carefully curating the sequence of these batches, an attacker can:Skew the local minima: Force the Deep Learning Projects for Final Year model's optimization path toward a specific, biased result.Embed a "Backdoor": Ensure that while the model looks accurate overall, it fails on specific, targeted triggers.
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