Recurrent Neural Networks & Sequence Models — Deep Learning & Neural…
Architectures designed for data with order, like text, audio and time series
Steps in Recurrent Neural Networks & Sequence Models
- RNN Fundamentals — advanced · How a recurrent network maintains a hidden state across a sequence
- The Vanishing Gradient Problem in RNNs — advanced · Why plain RNNs struggle to learn long-range dependencies
- Long Short-Term Memory (LSTM) — advanced · Gating mechanisms that let RNNs remember information over long sequences
- Gated Recurrent Units (GRU) — advanced · A simpler gating alternative to LSTM with fewer parameters
- Sequence-to-Sequence Models — advanced · Encoder-decoder architectures for tasks like translation where input and output lengths differ
- Introduction to the Attention Mechanism — advanced · Letting a decoder look back at all encoder states instead of a single fixed vector
Part of
- Deep Learning & Neural Networks roadmap — the full learning path