4-Recurrent Neural Networks
This is Lecture 4 of the series. Although the title says "Recurrent Neural Networks," this material actually covers everything from NLP fundamentals to word vectors and sequence models, so it effectively serves a dual purpose: first explaining the basic landscape of text tasks, then showing why RNNs, LSTMs, GRUs, and attention mechanisms are needed for processing sequential data.
What This Lecture Covers
This lecture is more complete than a pure model lecture because it lays out the task background before diving into model design.
- Language Processing Technology: introduces basic NLP concepts, as well as word-level, sentence/document-level analysis, and typical application scenarios.
- Word Vector Learning: transitions from discrete word representations to continuous vector representations, covering hierarchical softmax, negative sampling, and sentence vectors.
- Recurrent Neural Networks: explains RNN, LSTM, GRU, and attention mechanisms to help you understand the core ideas behind sequence modeling.
- Applications and Practice: places models back into real-world tasks, including text classification and movie review sentiment analysis.
How to Study This
- If your previous experience is mainly with image models, the most valuable thing to take from this lecture is a sense of "sequence" and "context" -- that is, why models need to retain historical information.
- When studying word vectors, do not treat them as just a trick. They fundamentally solve the problem that text input cannot be directly numerical, and they are a prerequisite for subsequent models to work.
- When studying RNN, LSTM, and GRU, focus on comparing how they handle long-term dependencies and information propagation, then combine this with sentiment analysis tasks to understand why these architectures are useful.