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Deep Learning & Computer Vision (Practice)
Train CNNs with strong discipline: augmentation, imbalance, transfer learning, and reliable training/debugging patterns.
Deep LearningComputer VisionCNNPyTorch/KerasAugmentation
Duration
4 days
Format
Remote
Level
Intermediate
Key outcomes
- Build and train CNN models with proper evaluation
- Use transfer learning efficiently for business datasets
- Handle class imbalance and noisy labels
- Improve robustness with augmentation and monitoring
Syllabus
Day 1 — Training fundamentals
- Loss, optimizers, schedules, early stopping
- Regularization: weight decay, dropout
- Debugging training and monitoring curves
Day 2 — CNNs & transfer learning
- Convolutions, pooling, normalization
- Pretrained backbones and fine-tuning strategy
- Feature extraction vs full fine-tuning
Day 3 — Real-world issues
- Augmentation strategies
- Imbalance: sampling, focal loss (concept), thresholds
- Error analysis: confusion matrix, failure clusters
Day 4 — Delivery patterns
- Exporting models and inference basics
- Batch inference vs real-time constraints
- Reproducibility, seeds, and experiment tracking