In the previous article, we just got started with the concept Word2Vec and assigning numbers to words. We will now see how to represent it in the form of a neural network.
To show how we can get a neural network to figure out what numbers should go with different words, let’s imagine we have two phrases:
- The Incredibles is great!
- Despicable Me is great!
To create a neural network that figures out what numbers we should associate with each word, the first thing we do is create inputs for each unique word.
For simplicity, assume the movie name is treated as a single word.
In this case, we have 4 unique words in the training data, so we create 4 input nodes.
Next, each input is connected to at least one activation function.
These activation functions use the identity function, which means the input value is exactly the same as the output value.
The number of activation functions corresponds to how many numbers we want to associate with each word.
The weights on these connections are the actual numbers we associate with each word.
In this example, we want to associate two numbers with each word.
These weights initially start with random values. During training, they are optimized using backpropagation, which we will explore in the next article.
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