6 articles in this category.
The practical layer between sequence-model theory and a working PyTorch classifier — building a vocabulary, batching variable-length sentences, packing sequences for the LSTM, and pooling the output back into a single vector.
A complete beginner's guide to transfer learning in NLP - how pretrained models work, why freezing encoders makes sense, and how to use sentence transformers effectively.
A deep dive into pretrained sentence embeddings, MLP architecture, BatchNorm, Dropout, Adam, and early stopping — with full PyTorch implementation.
Before reaching for LLMs or neural networks for text classification, try the boring thing. Here's how TF-IDF + Logistic Regression works, why it's often embarrassingly competitive, and where it breaks.
Learn how to add semantic caching to your RAG pipeline for lower latency and cost, then measure quality with RAGAS evaluation metrics.
A comprehensive guide to understanding the Transformer architecture that powers GPT, BERT, and other modern language models.