we will train a classifier on a binary prediction task: “is context words \(c_{1:L}\) likely to show up near some target word \(W_0\)?”

We estimate the probability that \(w_{0}\) occurs within this window based on the product of the probabilities of the similarity of the embeddings between each context word and the target word.

• we have a corpus of text
• each word is represented by a vector
• go through each position \(t\) in the text, which has a center word \(c\) and set of context words \(o \in O\)
• use similarity of word vectors \(c\) and \(o\) to calculate \(P(o|c)\)

Meaning, we want to devise a model which can predict high probabilities \(P(w_{t-n}|w_{t})\) for small \(n\) and low probabilities for large \(n\)

WPA is the largest relief program ever in the Great Depression New Deal, to promote public infrastructure and create artistic murals. It helped unskilled men to carry out public works infrastructure.

The project started 5/1935 and dissolved 6/1943.

if you can prove that for every \(i\), you can’t predict the

• vertibre backbone: 3 points to remember
• “we are in the business of looking for outliers”
• tarpit ides
• vision with world + good team

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