Natural Language Processing with PyTorch

Natural Language Processing with PyTorch

Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. When combined with PyTorch, NLP becomes flexible, powerful, and well-suited for building modern deep learning models used in real-world applications.

This topic focuses on applying deep learning techniques to text data, moving from classical NLP pipelines to advanced neural architectures using PyTorch.

Key Concepts Covered:

Text preprocessing: tokenization, normalization, stemming, and lemmatization

Word representations: Bag-of-Words, TF-IDF, Word2Vec, GloVe

Neural networks for NLP: RNNs, LSTMs, GRUs

Attention mechanisms and Transformers

Language models and text generation

Text classification, sentiment analysis, and named entity recognition (NER)

Sequence-to-sequence models and embeddings

Tools & Skills Gained:

PyTorch for building and training deep learning models

TorchText and NLP datasets handling

Training, evaluation, and optimization of NLP models

GPU acceleration and model experimentation

Understanding modern NLP architectures used in industry