AppliedAIPrep logoAppliedAI/Prep
← Machine Learning & Data Science / 29
mediumGoogleNVIDIAAmazon

How do RNNs, LSTMs, and GRUs work, and why did transformers largely replace them?

Sequence models are foundational and still asked, especially the gating that fixed RNNs and why attention won. The signal is connecting the vanishing-gradient story to the parallelism argument that let transformers ride the scaling wave.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Sequence models are foundational and still asked, especially the gating that fixed RNNs and why attention won. The signal is connecting the vanishing-gradient story to the parallelism argument that let transformers ride the scaling wave.

Unlock the other 754 answers · ₹2,000 / $25includes both full courses · progress stays saved · 6 months · one payment · no auto-renew
UP NEXT ON YOUR JOURNEY
Next in this trackHow do GANs work, why is training unstable, and why did diffusion overtake them?Next in this trackWhat are autoencoders and VAEs, and what is the reparameterization trick?Next in this trackWhat is transfer learning, and how do you decide whether to freeze, fine-tune, or use feature extraction?
DISCUSSION · 0

No comments yet — be the first to share your approach.