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@@ -271,7 +271,7 @@
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"id": "299baef3-b1a8-49ba-bad4-f62c8a416d83",
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"id": "299baef3-b1a8-49ba-bad4-f62c8a416d83",
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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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- "- (In this book, we follow the common machine learning and deep learning convention where training examples are represented as rows and feature values as columns; in the caase of the tensor shown above, each row represents a word, and each column represents an embedding dimension)\n",
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+ "- (In this book, we follow the common machine learning and deep learning convention where training examples are represented as rows and feature values as columns; in the case of the tensor shown above, each row represents a word, and each column represents an embedding dimension)\n",
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"\n",
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"\n",
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"- The primary objective of this section is to demonstrate how the context vector $z^{(2)}$\n",
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"- The primary objective of this section is to demonstrate how the context vector $z^{(2)}$\n",
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" is calculated using the second input sequence, $x^{(2)}$, as a query\n",
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" is calculated using the second input sequence, $x^{(2)}$, as a query\n",
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