PyTorch Explained: From Automatic Differentiation to Training Custom Neural Networks | Towards Data Science

pytorchdeep-learningautogradneural-networkspython

Abstraction: PyTorch core abstractions from tensors and autograd to transformer encoder

Key points:

Two foundational abstractions: tensors (multidimensional arrays with GPU support and computation graph tracking) and autograd (dynamic computation graph enabling automatic reverse-mode differentiation via .backward())
Standard training loop: forward pass → compute loss → loss.backward() → optimizer.step() → optimizer.zero_grad(); zero_grad prevents gradient accumulation across iterations
Optimizers (SGD, Adam, RMSprop) replace manual weight updates; Adam adds momentum and per-parameter adaptive learning rates for faster convergence on noisy gradients
nn.Module is the universal building block: subclass it, initialize layers in __init__, define forward(); PyTorch handles backward automatically if the graph is differentiable
Article walks through linear regression, MLP with ReLU, ResNet block with residual connections, and a full Transformer encoder (self-attention + layer norm + FFN)
For large datasets, Stochastic Gradient Descent on minibatches is preferred over the closed-form normal equation due to computational cost

Connections: Pytorch · Deep Learning · Automatic Differentiation · Transformers · Gradient Descent