MLDL 106 – Sequence Models (RNN, LSTM, GRU)

(Weeks 11–12 | Lecture 6 Hrs / Lab 18 Hrs / Ext 0 Hrs | 24 Total Contact Hrs | 1.0 Semester
Credit)
Students will:

• Build sequential models using RNN, LSTM, and GRU architectures.
• Design solutions for time-series forecasting and sequence classification.
• Handle challenges like vanishing gradients and overfitting in sequential data.
  Prerequisite: MLDL 105 – Computer Vision with CNNs
  Tools: TensorFlow, Hugging Face