Module 8 · PyTorch Deep Learning

Module 8: RNNs & Sequence Modeling

Examine Recurrent Neural Networks models for timeseries sequences. Code hidden state propagation loops, and unrolled gradients BPTT.

⏱ 24 Min Read • Author: GenAIWallah Team • Updated: May 2026

Day 15

Recurrent Neural Networks (RNNs)

Why this matters

RNN Basics: RNNs maintain hidden state across time steps — foundation for sequences before Transformers.

RNNs pass a hidden state between time steps: h_t = f(x_t, h_{t-1}). nn.LSTM mitigates vanishing gradients with gating.

Input shape often (seq_len, batch, features) with batch_first=True optional.
Pack padded sequences for variable-length batches (pad_sequence).

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain rnn basics in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define RNN Basics and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating RNN Basics.
Advanced: Intentionally break RNN Basics and interpret the error.

Recap

You can explain rnn basics clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Sequence Training

Day 16

Sequence Training Loops and Unrolling

Why this matters

Sequence Training: BPTT unrolls sequences through time; truncated BPTT trades memory for long sequences.

Backpropagation through time (BPTT) unrolls the RNN over sequence length and backprops through all steps. Truncated BPTT limits unroll depth for long sequences.

Track complete: You have covered tensors → autograd → training → data → GPU → optimizers → CNNs → RNNs. Next steps: Lightning/Hugging Face, Transformers, and deployment.

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain sequence training in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define Sequence Training and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating Sequence Training.
Advanced: Intentionally break Sequence Training and interpret the error.

Recap

You can explain sequence training clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Back to PyTorch Hub

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