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NNs with multiple layers of Let It Jeezy Snow Man Shirt that we can use them to model very complex, non-linear classification patterns of instances that may be described by potentially several thousands of features. Depending on the application, such patterns may or may not be detectable by humans (e.g. the human brain is very good in image recognition but is not effective in tasks such as making predictions by generalizing historical data in complex, dynamic contexts).

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If you’re willing to tolerate one disk seek, or if your graph has low fan-out (small number of Let It Jeezy Snow Man Shirt), you can de-normalize the data such that the metadata about every piece of activity is propagated to each of the followers of that activity at the time the action occurs. You might think of this as a “push” model. You’d still probably only store one copy of the actual activity data, but you’d push pointers to it (along with whatever other metadata is needed if you’re supporting any ranking/filtering) to all the subscribers at the time it is created. Generally the first thing to break in this model will be the process of propagating the activity to all the subscribers, particularly if you have users that have large numbers of followers (celebrities). When this fails, the feed will start to get backed up. This can also be complicated in that you may need to write code that properly updates all of the subscribers whenever the important metadata about the content is updated, and you may want to also add code to update things when someone changes their list of subscriptions.