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Q1: Lecture 1 Introduction and Word Vectors
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Q20: Lecture 20: Future of NLP + Deep Learning
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Q19: Lecture 19: Bias in AI
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Q18: Lecture 18: Constituency Parsing, TreeRNNs
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Q17: Lecture 17: Multitask Learning
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Q16: Lecture 16: Convolutional Networks for NLP
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Q15: Lecture 15: Natural Language Generation
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Q14: Lecture 14: Transformers and Self-Attention
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Q13: Lecture 13: Contextual Word Embeddings
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Q12: Lecture 12: Subword Models
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Q11: Lecture 11: Convolutional Networks for NLP
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Q10 : Lecture 10: Question Answering
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Q9 : Lecture 9: Practical Tips for Projects
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Q7: Lecture 7: Vanishing Gradients, Fancy RNNs
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Q6: Lecture 6: Language Models and RNNs
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Q5: Lecture 5: Dependency Parsing
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Q4: Lecture 4: Backpropagation
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Q2:Lecture 2 Word Vectors and Word Senses
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