Welcome to our tutorials on deep learning for natural language processing (NLP). This section is dedicated to providing in-depth guides and resources to help you understand and apply deep learning techniques in the field of NLP.
Overview
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages. Deep learning has revolutionized the field of NLP by enabling computers to process and understand human language with high accuracy.
Tutorials
Introduction to Deep Learning in NLP
- Learn the basics of deep learning and how it applies to NLP.
- Understand the concepts of neural networks and recurrent neural networks (RNNs).
Word Embeddings and Vector Spaces
- Explore the use of word embeddings like Word2Vec and GloVe.
- Discover how to represent words as dense vectors and use them for various NLP tasks.
Sequence Models and Recurrent Neural Networks (RNNs)
- Understand the architecture and working of RNNs.
- Learn how to implement RNNs for tasks like language modeling and machine translation.
Convolutional Neural Networks (CNNs) for NLP
- Explore the application of CNNs in NLP tasks.
- Understand how CNNs can be used for tasks like sentiment analysis and text classification.
Transformers and BERT
- Learn about the transformer architecture and its applications.
- Understand the BERT model and its use in various NLP tasks.
Resources
For further reading, we recommend visiting our Deep Learning for NLP Resources section.