Learn PYTORCH with Real Code Examples
Updated Nov 24, 2025
Architecture
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
Rendering Model
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
Architectural Patterns
Layer-based neural networks
Data pipeline using Dataset/DataLoader
Custom training loops for flexibility
Distributed and parallel training support
TorchScript/ONNX for production deployment
Real World Architectures
CNNs for images
RNNs, LSTMs, Transformers for sequences
Reinforcement learning agents
GANs and VAEs for generative modeling
Multi-modal learning combining text, image, audio
Design Principles
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)
Scalability Guide
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
Migration Guide
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