17 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.
A deep dive into Recurrent Neural Networks and Long Short-Term Memory networks. Learn how they process sequences, why vanilla RNNs fail, and how LSTMs solve the vanishing gradient problem.
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 complete beginner's guide to ReLU (Rectified Linear Unit) - what it is, why it works so well, and how to use it in neural networks with clear examples.
A complete beginner's guide to the Adam optimizer - how it adapts learning rates per parameter, why it converges faster than SGD, and how to use it effectively in PyTorch.
Learn how backpropagation works through a simple, step-by-step example. Understand the chain rule intuitively with clear visualizations and working code.
A complete beginner's guide to Batch Normalization - what it is, why it works, how to implement it, and the critical train vs eval mode difference that trips up everyone.
A complete beginner's guide to Dropout regularization - why randomly turning off neurons makes neural networks smarter, how it works, and how to use it correctly in PyTorch.
A complete beginner's guide to early stopping - how to automatically find the optimal training duration, prevent overfitting, and save the best model weights.
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-friendly guide to PyTorch's autograd engine — from what a gradient is to building a neural network by hand.
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.
Tensors explained from scratch — no math degree required. Learn what tensors are, why PyTorch uses them, and how to work with them confidently.
A deep dive into pretrained sentence embeddings, MLP architecture, BatchNorm, Dropout, Adam, and early stopping — with full PyTorch implementation.
Build a multi-class classifier in PyTorch without nn.Linear, without optim.SGD, without CrossEntropyLoss. Just [tensors](/blog/what-is-a-tensor), [autograd](/blog/pytorch-autograd-deep-dive), and arithmetic — so you finally see what those helpers actually do.
A comprehensive guide to understanding the Transformer architecture that powers GPT, BERT, and other modern language models.