In the last post we built the data preprocessing required for word2vec training. In this post we will build the network and perform the training on the text8 dataset (source), a Wikipedia dump of ~17 million tokens.

Note that we are implementing the skip-gram version of word2vec since it has superior performance
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One of the trends I've been seeing is the use of embeddings for similarity/recommendation in non-NLP domains (this talk and many others). While I've used/thought about word2vec for a while, I wanted to implement this to really get a sense of how it works and ways it can be extended to other domains. Intuitively, word2vec makes sense, and there are a lot of packages that will let you compute it without thinking much about the implementation (e.g. gensim
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I've been working on text classification recently. I've found keras to be a quite good high-level language and great for learning different neural network architectures. In this notebook I will examine Tweet classification using CNN and LSTM model architechtures. While CNNs are widely used in Computer Vision, I saw a paper
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