Summary
In this module, you learned about the basics of Natural Language Processing, including text representation, embeddings, traditional recurrent network models, and generative networks. We focused mostly on text classification and didn't discuss in detail other important tasks like named entity recognition, machine translation, and question answering. To implement those tasks, the same basic RNN principles are used with a different top layer architecture. To get a more complete understanding of the NLP field, you should experiment with some of those problems as well.
We introduced the concept of contextual embeddings in the embeddings unit, noting that traditional embeddings like Word2Vec can't distinguish between different meanings of the same word. This limitation motivates the use of attention mechanisms and transformer architectures, which have largely superseded RNNs for many tasks. Models such as BERT use attention to capture relationships between all words in a sentence simultaneously, rather than processing them sequentially. Understanding the RNN foundations covered in this module help you appreciate why these newer architectures were developed.
Large language models (LLMs) such as the GPT family take text generation even further. These models can be prompted to solve different tasks just by providing an initial sequence, which has led to a paradigm shift in NLP. If you want to get serious about NLP, you should explore transformer-based models and the Hugging Face Transformers library, which provides easy access to many pretrained models.