# Writings

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!

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