Edited, memorised or added to reading queue
on 03-Nov-2019 (Sun)
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Unknown title
Word2Vec Tutorial - The Skip-Gram Model · Chris McCormick Chris McCormick About Tutorials Archive Word2Vec Tutorial - The Skip-Gram Model 19 Apr 2016 This tutorial covers the skip gram neural network architecture for Word2Vec. My intention with this tutorial was to skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details. Specifically here I’m diving into the sk
Word2Vec Tutorial - The Skip-Gram Model · Chris McCormick Chris McCormick About Tutorials Archive Word2Vec Tutorial - The Skip-Gram Model 19 Apr 2016 This tutorial covers the skip gram neural network architecture for Word2Vec. My intention with this tutorial was to skip over the usual introductory and abstract insights about Word2Vec, and get into more of the details. Specifically here I’m diving into the sk
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Unknown title
work, so we need a way to represent the words to the network. To do this, we first build a vocabulary of words from our training documents–let’s say we have a vocabulary of 10,000 unique words. <span>We’re going to represent an input word like “ants” as a one-hot vector. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that voc
work, so we need a way to represent the words to the network. To do this, we first build a vocabulary of words from our training documents–let’s say we have a vocabulary of 10,000 unique words. <span>We’re going to represent an input word like “ants” as a one-hot vector. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that voc
Flashcard 4514087832844
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Unknown title
tor. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. <span>The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. Here’s the architecture of our neural network. There is no activation function on the hidden layer neurons, but the output neurons use softmax. We’ll come back to this later. When train
tor. This vector will have 10,000 components (one for every word in our vocabulary) and we’ll place a “1” in the position corresponding to the word “ants”, and 0s in all of the other positions. <span>The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. Here’s the architecture of our neural network. There is no activation function on the hidden layer neurons, but the output neurons use softmax. We’ll come back to this later. When train