# Writings
Below you can find my writings on machine learning, data science, and
technology. Enjoy!
- [Further Double-checking FiveThirtyEight's 2016 Primary Predictions](/blog/re-double-checking-538/): Is FiveThirtyEight's Polls Plus model biased against any candidate? I continue my double-checking their model by looking at each candidate individually.
- [Lab41 Reading Group: Generative Adversarial Nets](/blog/lab41-generative-adversarial-nets/): What cost function would you use to determine if a picture looks real? How about one learned by another network! Find out more with my summary of Generative Adversarial Networks!
- [Double-checking FiveThirtyEight's 2016 Primary Predictions](/blog/double-checking-538/): How well did FiveThirtyEight do in predicting the primary results? I Double-check FiveThirtyEight's Polls Plus model by comparing its predictions to the outcomes of the 2016 primaries.
- [Python2Vec: Word Embeddings for Source Code](/blog/lab41-python2vec/): Parsing source code is easy; just let the interpreter do it! But what if you want to recommend code snippets? Then you need word embeddings, like my Python2Vec!
- [The Nine Must-Have Datasets for Investigating Recommender Systems](/blog/lab41-recommender-systems-datasets/): Do you want to play around with recommender systems, but you don't have any data? Don't worry, there are tons of great, open source datasets for recommender systems!