Intersections between NLP and ML:
- Neural Networks: NLP often uses neural networks to learn complex representations of text data.
- Word Embeddings: NLP uses word embeddings, such as Word2Vec and GloVe, to represent words as vectors.
- Sequence Models: NLP uses sequence models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, to process sequential data.
- Transfer Learning: NLP often uses pre-trained language models as feature extractors or fine-tunes them for specific tasks.
- Attention Mechanisms: NLP uses attention mechanisms to focus on specific parts of the input data.
By understanding these fundamentals, you'll be well-equipped to tackle a wide range of NLP and ML tasks and applications.