Back to AI DashboardModule 3: Sequence Models
AI Engineer Track
Module 3: Sequence Models
Understand recurrent memory architectures, LSTMs, and gated structures preceding modern attention.
Syllabus Modules
RNN FundamentalsComing Soon
Explore hidden state time steps loops and recurrent weight parameters.
Total Lessons: 0Explore Module
LSTM GatesComing Soon
Deconstruct cell states forget, input, and output gating equations.
Total Lessons: 0Explore Module
GRU SimplifiedComing Soon
Examine Gated Recurrent Units reset and update gate parameters.
Total Lessons: 0Explore Module
Seq2Seq ArchitecturesComing Soon
Map input sequence states to output translation matrices.
Total Lessons: 0Explore Module
Sequence ClassificationComing Soon
Classify sequence sequences mapping recurrent hidden states to labels.
Total Lessons: 0Explore Module
Track Progress
0 / 6Projects Verified
Learning Outcomes
- Build character prediction models using recurrent cells
- Trace gradient paths inside LSTM gating circuits
- Understand sequence-to-sequence translation architectures
Interview Defense
- Explain the vanishing gradient problem in vanilla RNNs vs LSTMs
- Compare seq2seq sequence decoding speed tradeoffs with parallel inputs