Seq2Seq & LSTM Encoder-Decoder Clearly Explained
How do you translate a sentence when the input length doesn't match the output length?
In this video, we break down the theory behind Sequence-to-Sequence (Seq2Seq) models. While standard Deep Neural Networks require fixed inputs and outputs, Seq2Seq allows us to map variable-length sequences (like an English sentence) to other variable-length sequences (like a French translation).
We explore the Encoder-Decoder architecture using LSTMs, tracing exactly how data flows from an input sequence, through a bottleneck Context Vector, and into a generated output.
In this video, you will learn:
• Why standard Neural Networks fail at translation tasks.
• How the Encoder compresses specific meaning into a Context Vector.
• How the Decoder uses Auto-regression to generate text one token at a time.
• The concept of Teacher Forcing to speed up training.
• The "Reverse Input" optimization trick used in the original 2014 paper.
Running Example: We visualize the step-by-step translation of "Hello World" (2 tokens) to "Bonjour le monde" (3 tokens) to demonstrate how the architecture handles length mismatch.
References & Papers:
• Sequence to Sequence Learning with Neural Networks (Sutskever et al., 2014)
• Based on concepts from StatQuest with Josh Starmer and Weights & Biases.