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lucatrevisan.wordpress.com | ||
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www.jeremykun.com
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| | | | | When addressing the question of what it means for an algorithm to learn, one can imagine many different models, and there are quite a few. This invariably raises the question of which models are "the same" and which are "different," along with a precise description of how we're comparing models. We've seen one learning model so far, called Probably Approximately Correct (PAC), which espouses the following answer to the learning question: | |
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algorithmsoup.wordpress.com
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| | | | | The ``probabilistic method'' is the art of applying probabilistic thinking to non-probabilistic problems. Applications of the probabilistic method often feel like magic. Here is my favorite example: Theorem (Erdös, 1965). Call a set $latex {X}&fg=000000$ sum-free if for all $latex {a, b \in X}&fg=000000$, we have $latex {a + b \not\in X}&fg=000000$. For any finite... | |
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grigory.github.io
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| | | | | Collection of interesting papers on algorithms for big data from 2016. | |
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www.softdevtube.com
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| | | The onion architecture is not as well known as the 3-tier software architecture but is gaining a lot of attention during the microservices era. It structures your software so that it is easy to change technologies without impacting business logic. Coupled with Domain Driven Design (DDD) principles it offers a powerful way to build a | ||
