10 articles with this tag.
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.
Learn how Bidirectional LSTM networks process text sequentially to capture context, word order, and meaning. A complete guide to building your first sequence model for NLP.
Master text preprocessing and tokenization for NLP. Learn about vocabulary building, padding, truncation, and handling variable-length sequences in deep learning models.
Learn how word embeddings transform words into meaningful numerical vectors. Understand one-hot encoding, learned embeddings, Word2Vec, GloVe, and when to use pretrained vs learned embeddings.
A beginner-friendly guide to pretrained sentence embeddings, multi-layer perceptrons, and the building blocks that make modern NLP work — explained with simple examples and zero jargon.
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.
Guide to text chunking strategies, why they matter, and how to pick the right one for your RAG project.
A comprehensive guide to understanding the Transformer architecture that powers GPT, BERT, and other modern language models.