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Compare up to three published courses by skills, level, duration, prerequisites and certification requirements.
| Compare | PyTorch for AI Engineering |
|---|---|
| Level | Intermediate |
| Duration | 20–30 hours |
| Format | Self-paced course |
| Access | Premium access |
| Skills | tensor creation, dtype, shape, device, reshape/view, indexing, operations, requires_grad, computation graph, backward(), gradients, no_grad/inference mode, nn.Module, Linear, activations, Sequential, forward method, parameters, CrossEntropyLoss, MSELoss, Adam, SGD, zero_grad, step, learning-rate basics, Dataset, DataLoader, batching, shuffle, collate basics, train/validation split, train/eval modes, metrics, checkpointing, early stopping intuition, logging, state_dict, checkpoint files, load_state_dict, device mapping, versioning, shape errors, device mismatch, NaNs, memory issues, data leakage, overfitting |
| Modules | 8 |
| Prerequisites | NumPy fluency; Multi-Layer Neural Networks |
| Assessment | Course assessment |
| Certificate | Completion requirements apply |