PyTorch course syllabus
Follow the lessons in order — each one builds on the last. Tick them off as you go.
- PyTorch Introduction & Tensors Install PyTorch, create and reshape tensors, move them to the GPU, and understand why PyTorch is the most popular deep learning framework. 4 min
- PyTorch Autograd Explained How PyTorch computes gradients automatically: requires_grad, the computation graph, backward(), and turning gradients off. 4 min
- Building Models with nn.Module Define neural networks as nn.Module classes, use layers and activations, and inspect parameters in PyTorch. 4 min
- Training Loop, Datasets & DataLoaders Write the standard PyTorch training loop with Dataset, DataLoader, a loss function and an optimizer, plus a validation pass. 5 min
- Saving, Loading & Running Inference Save and load models with state_dict, checkpoint training, and run fast inference with eval mode and torch.inference_mode. 4 min
- PyTorch Interview Questions Frequently asked PyTorch interview questions — tensors, autograd, nn.Module, training loops and deployment — with answers. 3 min
PyTorch FAQ
Is this PyTorch tutorial free?
Yes. All 6 PyTorch lessons are free to read online with no sign-up. Each one includes working code examples you can copy and run.
Is PyTorch good for beginners?
Yes. The tutorial starts with "PyTorch Introduction & Tensors" and builds up one concept at a time, so you don't need any prior PyTorch experience — just follow the lessons in order.
How long does it take to learn PyTorch?
Reading all 6 lessons takes about 1 hour. Most learners need a few weeks of hands-on practice alongside the lessons to feel confident using PyTorch on real projects.
What should I learn after PyTorch?
Good next topics are Artificial Intelligence, AI Agents, Deep Learning, LLM Fine-Tuning. The AI Engineer Roadmap shows the full order.