5 articles in this category.
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.
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.