We’re going to train a simple neural network with a single hidden layer to perform a certain task, but then we’re not actually going to use that neural network for the task we trained it on! Instead, the goal is actually just to learn the weights of the hidden layer–we’ll see that these weights are actually the “word vectors” that we’re trying to learn.
If you want to change selection, open document below and click on "Move attachment"
Unknown titleaks and enhancements that start to clutter the explanation. Let’s start with a high-level insight about where we’re going. Word2Vec uses a trick you may have seen elsewhere in machine learning. <span>We’re going to train a simple neural network with a single hidden layer to perform a certain task, but then we’re not actually going to use that neural network for the task we trained it on! Instead, the goal is actually just to learn the weights of the hidden layer–we’ll see that these weights are actually the “word vectors” that we’re trying to learn. Another place you may have seen this trick is in unsupervised feature learning, where you train an auto-encoder to compress an input vector in the hidden layer, and decompress it back t Summary
| status | not read | | reprioritisations | |
|---|
| last reprioritisation on | | | suggested re-reading day | |
|---|
| started reading on | | | finished reading on | |
|---|
Details