MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention - YouTube
This MIT lecture introduces sequence modeling using recurrent neural networks (RNNs). The class explores RNN intuition and design criteria for sequential data, including variable-length sequences and long-term dependencies. Practical examples and code implementations illustrate RNNs and their limitations, paving the way for more advanced architectures. Follow along using the transcript. This MIT lecture introduces sequence modeling using recurrent neural networks (RNNs). The class explores RNN intuition and design criteria for sequential data, including variable-length sequences and long-term dependencies. Practical examples and code implementations illustrate RNNs and their limitations, paving the way for more advanced architectures. Follow along using the transcript.
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