CS224N Review

1.[CS224N] Lecture 1: Introduction and Word Vectors

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2.[CS224N] Lecture 2: Word Vectors and Word Senses

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3.[CS224N] Lecture3: Word Window Classification, Neural Networks, and Matrix Calculus

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4.[CS224N] Lecture 4: Back Propagation and Computation Graphs

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5.[CS224N] Lecture5: Dependency Parsing

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6.[CS224N] Lecture 6: Language Models and Recurrent Neural Network

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7.[CS224N] Lecture 7: Vanishing Gradients, Fancy RNNs

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8.[CS224n] Lecture 9: Practical Tips for Projects

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9.[CS224n] Lecture 8: Translation, Seq2Seq, Attention

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10.[CS224n] Lecture 12: Subword Models

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11.[CS224n] Lecture 15: Natural Language Generation

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12.[CS224n] Lecture 18: Constituency Parsing, TreeRNNS

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13.[CS224n] Lecture 14: Transformers and Self-Attention for Generative Models

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14.[CS224n] Lecture 13: Contextual Word Embeddings

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