16 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.
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.
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.