PyTorch Tutorial with Examples.
PyTorch tutorial with examples: tensors, autograd, nn.Module, training loops, distributed training and TorchServe for deep learning.
Beginner
Start here — no prior experience needed
PyTorch Learning Roadmap
A structured path from tensor basics to training and deploying custom neural networks at scale.
Introduction to PyTorch
Learn PyTorch tensors, views, dtypes, and autograd through small runnable examples, then train a two-layer network on XOR with nothing but raw tensors.
PyTorch Neural Networks with nn.Module
Build networks with nn.Module and nn.Sequential, pick the right loss and optimizer, write the standard training loop, and save and restore checkpoints.
PyTorch Projects
Projects that take you from tensor basics to training and deploying custom neural networks — covering vision, NLP, and production deployment.
Intermediate
For developers with core concepts down
PyTorch Convolutional Neural Networks
Build CNNs for image classification in PyTorch — convolution shapes, weight sharing, a 99% MNIST model trained on CPU, and transfer learning from ResNet.
RNNs and LSTMs in PyTorch
Build sequence models — LSTM and GRU shapes, packed sequences for padded batches, a text classifier, and a forecaster checked against a baseline.
PyTorch Custom Datasets and DataLoaders
Build PyTorch data pipelines — custom Dataset classes, splits, weighted sampling, collate functions, and sharding IterableDataset workers.
Advanced
Production-grade patterns for experienced engineers
PyTorch Training Best Practices
Mixed precision, gradient scaling and clipping, gradient accumulation, LR warmup, exact checkpoint resume, and torch.compile in the PyTorch training loop.
PyTorch Deployment with ONNX
Export PyTorch models to ONNX, verify them against PyTorch, serve with ONNX Runtime, measure latency, and replace TorchScript with torch.export.
PyTorch Dynamic Quantization
Shrink a trained model 4x by converting its weights to int8 after training — and find out why that doesn't always mean faster inference.
Transformers in PyTorch
Understand Transformers: attention by hand, padding and causal masks, multi-head attention, positional embeddings, and the Hugging Face API.
PyTorch Distributed Training
Scale PyTorch training with DistributedDataParallel and torchrun — collectives, DistributedSampler, correct metrics, no_sync, and gradient checkpointing.