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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

How it works...

We begin Steps 1-3 by preparing and loading the MNIST dataset. Next, in Step 4, we import DPGradientDescentGaussianOptimizer, an optimizer that allows the model to become differentially private. A number of parameters are used at this stage, and these stand to be clarified. The l2_norm_clip parameter refers to the maximum norm of each gradient computed on an individual training datapoint from a minibatch. This parameter bounds the sensitivity of the optimizer to individual training points, thereby moving the model toward differential privacy. The noise_multiplier parameter controls the amount of random noise added to gradients. Generally, the more noise, the greater the privacy. Having finished this step, in Step 5, we define ...

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Publisher Resources

ISBN: 9781789614671Supplemental Content