PyTorch Mastery
Build and train neural networks with PyTorch from beginner to expert. Master PyTorch, the dominant deep learning framework in research and increasingly in production.
Level: Intermediate · Category: Tools & Frameworks · Estimated time: 8 hours
Prerequisites
- Neural Networks Deep Dive
Lessons
- Tensors & Operations — Creating tensors, shapes, dtypes, device management (CPU/GPU), and tensor math.
- Autograd & Computational Graphs — Automatic differentiation, requires_grad, backward(), and gradient accumulation.
- Building Models with nn.Module — Creating custom models, layers, forward(), and parameter management.
- Loss Functions & Optimizers — Cross-entropy, MSE, BCELoss, and using torch.optim effectively.
- Data Loading & Datasets — Dataset, DataLoader, transforms, and custom data pipelines.
- Training Loop Best Practices — Training/validation loops, checkpointing, logging, and debugging.
- CNNs in PyTorch — Conv2d, pooling, building image classifiers, and transfer learning.
- RNNs & Sequence Models — RNN, LSTM, GRU for sequential data processing.
- Mixed Precision & Performance — torch.amp, GPU optimization, profiling, and multi-GPU basics.
- Model Export & Deployment — TorchScript, ONNX export, and serving models.
Topics covered
pytorch, deep-learning, python, tensors, gpu, autograd