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Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Explores the evaluation of natural language generation models, emphasizing the importance of human judgments and the limitations of content overlap metrics.
Explains the full architecture of Transformers and the self-attention mechanism, highlighting the paradigm shift towards using completely pretrained models.
