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Artificial neural networks imitate concepts that we use when we think of the human brain.

We'll use embeddings and recurrent neural networks for sentiment classification of reviews from movies: we want to know if they contain a positive or negative sentiment. The

For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer '3' encodes the 3rd most frequent word in the data.

This allows for quick filtering operations such as: 'only consider the top 10,000 most common words, but eliminate the top 20 most common words As a convention, '0' does not stand for a specific word, but instead is used to encode any unknown word.

worst mistake of my life br br i picked this movie up at target for 5 because i figured hey it's sandler i can get some cheap laughs i was wrong completely wrong mid way through the film all three of my friends were asleep and i was still suffering worst plot worst script worst movie i have ever seen i wanted to hit my head up against a wall for an hour then i'd stop and you know why because it felt damn good upon bashing my head in i stuck that damn movie in the <UNK>

and watched it burn and that felt better than anything else i've ever done it took american psycho army of darkness and kill bill just to get over that crap i hate you sandler for actually going through with this and ruining a whole day of my life Note the special words like <START>

cut reviews after 80 words and pad them if needed: Now that we have our data, let's discuss the concepts behind our model!

embedding layer learns the relations between words, the recurrent layer learns what the document is about and the dense layer translates that to sentiment.

With one-hot encoding, the vocabulary '\(\textsf{code - console - cry - cat - dog}\)' would be represented like this: The three text snippets '\(\textsf{code console}\)', '\(\textsf{cry cat}\)' and '\(\textsf{dog}\)' are represented by combining these word vectors: This representation has some problems.

Instead of learning from one-hot encoding, we first let the neural network embed words in a smaller, continuous vector space where similar words are close to each other. The

Such an embedding for our vocabulary could look like this: We only need two dimensions for our words instead of five, '\(\mathsf{cat}\)' is close to '\(\mathsf{dog}\)', and '\(\mathsf{console}\)' is somewhere between '\(\mathsf{code}\)' and '\(\mathsf{cry}\)'. Closeness

If we'd be interested in understanding a document like in the previous example, we could use the following architecture: The left side of the figure shows a short-hand of the neural network, the right side shows the unrolled version.

The first layer learns a good representation of words, the second learns to combine words in a single idea, and the final layer turns this idea into a classification. We

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