Below you can find my writings on machine learning, data science, and technology. Enjoy!
Lab41 Reading Group: Deep Networks with Stochastic Depth
Dropout successfully regularizes networks by dropping nodes, but what if we went one step further? Find out how stochastic depth improves your network by dropping whole layers!
SAT2Vec: Word2Vec Versus SAT Analogies
Could Word2Vec pass the SAT analogies section and get accepted to a good college? I take a pre-trained model and find out!
Dragon Farkle: Simulating the End Game
How many soldiers do you need to successful defeat the dragon in Dragon Farkle, and how likely to succeed is your attack? I find out by simulating a game of Dragon Farkle!
Further Double-checking FiveThirtyEight's 2016 Primary Predictions
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
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
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
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
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!