Learn PYTORCH with Real Code Examples

Tensors: core data structure for computation

Autograd engine: computes gradients automatically

nn.Module: base class for defining neural network layers

Dynamic computation graphs: create operations on-the-fly

Optimizers and loss functions handle model training

Dynamic computation graphs built on-the-fly

Autograd for gradient calculation

Layer stacking via nn.Module

Forward/backward propagation loop

Hardware acceleration on CPU/GPU

Layer-based neural networks

Data pipeline using Dataset/DataLoader

Custom training loops for flexibility

Distributed and parallel training support

TorchScript/ONNX for production deployment

CNNs for images

RNNs, LSTMs, Transformers for sequences

Reinforcement learning agents

GANs and VAEs for generative modeling

Multi-modal learning combining text, image, audio

Pythonic and flexible interface

Dynamic computation graphs by default

Seamless GPU acceleration

Strong ecosystem for research and deployment

Integration with high-level libraries (Lightning, HuggingFace)

Use DataLoader with multiple workers

Leverage GPUs for training

Use mixed precision for memory efficiency

Distributed training with torch.distributed

Profile performance to optimize memory and computation

Update to latest stable PyTorch version

Replace deprecated APIs

Refactor custom layers for TorchScript if needed

Check device compatibility (CPU/GPU)

Validate models on new versions before deployment