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on 12-Jul-2019 (Fri)

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Annotation 4240800877836

Optionals ensure that nil values are handled explicitly.
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Flashcard 4241528589580

Question
Optionals ensure that nil values are handled explicitly.
Answer
[default - edit me]

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Annotation 4242125491468

and it continues to evolve
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Flashcard 4244677463308

Question
Historically , the answer to what makes us human, what makes us deal with the world in the ways we do,
Answer
has been that we have a different kind of intelligence than other animals.

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Annotation 4244679822604

A very different answer to what makes us human is provided in this book. Yes, humans have a dif- ferent kind of intelligence than other animals, but we also have a different kind of motivation. The human motivation for shared reality— the motiva- tion to share our feelings, thoughts, and concerns with others— is unique to humans. It is captured in “I wish you were here.”
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Annotation 4244684279052

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.
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aks 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



Annotation 4244686376204

Given a specific word in the middle of a sentence (the input word), look at the words nearby and pick one at random.
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ural network to perform, and then we’ll come back later to how this indirectly gives us those word vectors that we are really after. We’re going to train the neural network to do the following. <span>Given a specific word in the middle of a sentence (the input word), look at the words nearby and pick one at random. The network is going to tell us the probability for every word in our vocabulary of being the “nearby word” that we chose. When I say "nearby", there is actually a "window size" paramet