NVIDIA · NCA-GENM

The compute-efficiency thread

Getting more accuracy per unit of compute. Opens in M1 with training-stability levers (normalization, LR warmup, gradient clipping), and concentrates in M5's mixed precision, quantization, and pruning before M6 puts TensorRT and Triton underneath the served pipeline.

NCAM-T3 · 24 lessons across 3 modules

  1. M1M1-01Machine learning fundamentals: learning paradigms, feature engineering, and cross-validation
  2. M1M1-02Overfitting, underfitting, and the bias-variance tradeoff
  3. M1M1-03Model comparison metrics: accuracy, precision, recall, F1, ROC-AUC, MAE, MSE, R²
  4. M1M1-04Deep learning frameworks: TensorFlow, PyTorch, and Keras
  5. M1M1-05Neural network basics: neurons, activation functions, and the training loop
  6. M1M1-06Convolutions and the building blocks of vision models
  7. M1M1-07Nonsequential networks and residual connections
  8. M1M1-08Multimodal loss functions: cross-entropy, contrastive, reconstruction, adversarial, and composite
  9. M1M1-09Training stability in multimodal settings: normalization, LR warmup, loss weighting, gradient clipping
  10. M1M1-10Multimodal transfer learning: pretrained encoders, full fine-tuning vs. parameter-efficient adaptation
  11. M1M1-11Model fusion and orchestration: early, intermediate, and late fusion; modality vs. agent orchestration
  12. M1M1-12Prompt engineering fundamentals and emerging multimodal trends
  13. M5M5-01Mixed-precision training: FP16, FP32, loss scaling, and Tensor Cores
  14. M5M5-02Quantization: PTQ vs. QAT
  15. M5M5-03Neural network pruning: structured vs. unstructured
  16. M5M5-04Hyperparameter tuning: grid, random, and Bayesian search
  17. M5M5-05Transfer learning for efficiency
  18. M5M5-06Energy efficiency and inference optimization with TensorRT and Triton
  19. M6M6-01U-Net architecture: encoder-decoder structure and skip connections
  20. M6M6-02The U-Net as diffusion denoising backbone and as an autoencoder
  21. M6M6-03CLIP plus diffusion: building a text-to-image pipeline
  22. M6M6-04NVIDIA SDKs: NeMo, Riva, Triton, ACE, cuDNN, and AI Blueprints/VIA
  23. M6M6-05Prompt engineering for generative systems, and software quality practices
  24. M6M6-06Putting it together: a text-to-image service end to end

Part of the throughlines running across the NCA-GENM prep course.