Phase 3 — PyTorch Deep Learning
Weeks: 7–9 | Goal: Master PyTorch from tensors to distributed training; GPU/CUDA proficiency
Labs
| Lab | Topic | Key Skills |
|---|---|---|
| lab-01-pytorch-tensors-autograd | Tensors, autograd, custom backward | CUDA, mixed precision |
| lab-02-training-loop | DataLoader, training loop, optimizers | AMP, gradient accumulation |
| lab-03-cnn-from-scratch | Build ResNet-like CNN | BatchNorm, skip connections |
| lab-04-transfer-learning | Fine-tune pretrained models | Feature extraction vs fine-tuning |
| lab-05-distributed-training | DDP, gradient accumulation | Multi-GPU scaling strategies |
GPU/CUDA Fundamentals
This phase covers:
- CUDA device management (
torch.device,.cuda(),.to(device)) - Mixed precision training (
torch.cuda.amp.autocast,GradScaler) - Memory management (
torch.cuda.empty_cache(),torch.no_grad()) - Profiling (
torch.profiler,nvidia-smi) - DataParallel vs DistributedDataParallel (DDP)
Why PyTorch for CV Engineers
"If you can't implement it in PyTorch, you don't understand it."
Every SOTA CV model (YOLO, SAM, CLIP, ViT) ships in PyTorch. Debugging gradient issues, optimizing training throughput, and serving with TorchScript requires deep PyTorch fluency — not just calling .fit().