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Articles

(Oct 27) #optimization #debugging #windows

You can't reallly optimize memory usage in a program if you don't know where memory is being used or leaked the most. Heap snapshots are a great tool for this, since they show you where the memory in a process is locked up. In this code-sparse, but thorough introductory article, Bruce Dawson explains what heap snapshots are, how to use them, and explores some traces in Windows.

(Oct 26) #machine learning #python #data streams

One of the biggest drawbacks of machine learning is how time consuming handling and processing the copious amounts of data it often needs is. A small optimization in the code can have a substantial effect on the time wasted. In Guillaume Chevalier's article, he painstakingly covers pipelines in ML: what they are, what they must include, the pros and cons of using checkpoints in the pipeline, managing state and cache, data streaming, and object oriented programming encapsulation tradeoffs.

(Oct 27) #math #python #audio

Fourier tranforms (FTs) are a great tool for processing data, since they allow you to switch between the time and frequency domain fairly easily for a set of data. This is especially useful for all things audio related. In this nifty article, Nolan Nicholson highlights the concepts behind why the FT is useful for signal processing, how to use it to find the pattern in a video game song, and where to loop, all implemented in Python.

(Oct 25) #math # statistics # python

Markov Chain Monte Carlo (MCMC) methods are mostly used for estimating the value of an integral with lots of unknown variables. They are applicable in Bayesian statistics and computational physics/biology/linguistics. Sounds complicated? Well luckily in this article Simeon Carstens does an excellent job of introducing Markov Chains, implementing the complete Metropolis-Hastings algorithm in Python, and exploring its behavior, all to give you a better understanding of this deeply mathematical concept.

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