7 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.
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 early stopping - how to automatically find the optimal training duration, prevent overfitting, and save the best model weights.
A complete, beginner-friendly guide to PyTorch's autograd engine — from what a gradient is to building a neural network by hand.
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.