University · Data Science · Deep Learning and Neural Networks

Recurrent Networks and Transformers for Sequences

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RNNs, vanishing gradients, LSTM, GRU, seq2seq, attention mechanism, transformer architecture, self-attention, positional encoding, BERT, GPT

Inhaltsübersicht

  • Recurrent Neural Networks and the Vanishing Gradient Problem
  • LSTM and GRU: Gated Architectures for Long-Range Dependencies
  • Sequence-to-Sequence Models and the Attention Mechanism
  • The Transformer Architecture, Self-Attention, and Foundation Models
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Learn Recurrent Networks and Transformers for Sequences — Deep Learning and Neural Networks Data Science | Summary, Flashcards & Quiz