7-Deep Learning Applications (Natural Language Processing)
This is Lecture 7 of the series. It can be seen as an application extension of the earlier "Recurrent Neural Networks" lecture. Starting from the full NLP task landscape, it progressively leads to problems closer to real systems, such as sentiment analysis, reading comprehension, question answering, text generation, multimodal learning, and machine translation.
What This Lecture Covers
The value of this lecture lies in expanding natural language processing from single-model learning to a task-oriented and application-oriented perspective.
- NLP Overview: first presents the basic concepts and technical overview of NLP to help establish task boundaries.
- Advanced NLP: covers core tasks such as sentiment analysis, machine reading comprehension, automatic question answering, and text generation.
- Multimodal Fusion: introduces directions like multimodal classification and multimodal retrieval, extending the task scope from pure text to cross-modal scenarios.
- Applications and Practice: supplements more engineering-oriented content such as optimization, hyperparameter tuning, and visualization, with machine translation as the practical case.
How to Study This
- When studying this lecture, the focus should be on "task formulations" rather than individual model names. Different tasks often differ significantly in their input/output formats, evaluation metrics, and training methods.
- If you have not yet worked on an NLP project, it is recommended to first thoroughly understand tasks with clearer boundaries, such as sentiment analysis, text classification, and machine translation, before tackling more complex question answering and generation.
- When you reach the multimodal section, think of it as "text no longer exists in isolation." This perspective will be very helpful when you later study vision-language models.