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Update app.py
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app.py
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@@ -1131,15 +1131,15 @@ if query:
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# Add links to videos and webpages
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# Add links to videos and webpages
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st.markdown("""
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# else:
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# Add links to videos and webpages
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# Add links to videos and webpages
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st.markdown("""
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Here are some useful resources to help you learn more about Word2Vec:
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1. [Word2Vec Tutorial - The Skip-Gram Model](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) - A blog post by Chris McCormick providing a detailed explanation of the skip-gram model used in Word2Vec.
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2. [Word2Vec Tutorial Part 2 - Negative Sampling](http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/) - A follow-up blog post by Chris McCormick discussing negative sampling in Word2Vec.
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3. [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf) - The original research paper by Mikolov et al. that introduced the Word2Vec algorithm.
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4. [Word2Vec Tutorial: Vector Representation of Words](https://www.youtube.com/watch?v=64qSgA66P-8) - A YouTube video by Sentdex explaining the Word2Vec algorithm and its implementation in Python.
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5. [Word2Vec: How to Implement Word2Vec in Python](https://www.youtube.com/watch?v=ISPId9Lhc1g&t=6s) - A YouTube video by Data Talks demonstrating how to implement Word2Vec in Python using the Gensim library.
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6. [Cosine Similarity Calculator](https://www.omnicalculator.com/math/cosine-similarity) - A calculator for computing cosine similarity, a common metric used in measuring similarity between vectors.
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""")
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# else:
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