Author Archives: underwhelmingforce

Adventures in Reinforcement Learning: Trials and Errors

Over the last few weeks, I’ve been working on learning some basic reinforcement learning models. All my code is available here (warning: basically totally undocumented and uses code from a few sources. It’s not the prettiest). The Environment The environment I … Continue reading

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Unexpected results in ML & project updates

There was a recent paper that caught my eye that I think is worth sharing with anyone interested in the themes of my blog: The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial … Continue reading

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Sentiment Translator

Introduction A very common problem in natural language processing is that of sentiment analysis, or rather, attempting to analytically determine if a given input has an overall positive or negative tone. For example, a positive string might be “I really enjoyed this movie!” whereas … Continue reading

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Automated topic extraction with Latent Dirichlet Allocation

During last semester, I became aware of a really interesting NLP algorithm called LDA, short for “Latent Dirichlet Allocation.” The goal of the algorithm is to create a set of “topics” which represent a specific human-interpretable concept The core assumption … Continue reading

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Making art with neural style and fractals

I recently attempted to see if I could create art with Neural Style using only photos I’ve taken and fractal flames I’ve created with Fractorium and Apophysis. I must say, I’m very happy with the results! I generated the outputs … Continue reading

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Steam Games

While I work on my next big project, I decided to generate some random steam game names. All of these are games that don’t actually exist: Happy Panic Unraveled Land Mad Sharkness The Heart’s Medicine formic innocence Heroes Over Europe … Continue reading

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Espresso is the marshmallow of coffee (fun with Word2Vec)

In exploring the recipe dataset, I decided to have some fun with Word2vec, an algorithm originally created by Google. For the layperson, this algorithm works by looking at the context in which a given word appears and learns vectors to … Continue reading

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