Frontier architectures & systems: Diffusion Models, Transfer Learning, PEFT/LoRA, PyTorch Internals, Deployment/ONNX, Multimodal AI, and Mixture of Experts.
Mathematical foundations and implementations of Denoising Diffusion Probabilistic Models (DDPM): forward Gaussian noise scheduling, reverse denoising U-Net, Score-based models, and latent diffusion.
Leveraging pretrained representations: feature extraction vs full fine-tuning, strategic layer freezing, learning rate warmups, and domain adaptation techniques.
State-of-the-art parameter-efficient adaptation: Low-Rank Adaptation (LoRA) matrix decomposition, rank selection, alpha scaling, 4-bit NormalFloat QLoRA, and Prefix Tuning.
Mastering the PyTorch engine: tensor memory layouts, autograd computational graphs, custom nn.Module building blocks, dataset/DataLoader pipelining, and multi-GPU training.
Production training methodologies: train/val/test data leakage prevention, cross-validation, learning rate schedulers, early stopping, and metric evaluation (ROC-AUC, F1, PR curves).
Deploying models to production: serialization checkpoints, TorchScript JIT tracing, ONNX graph export, INT8/FP16 quantization, TensorRT acceleration, and low-latency serving.
End-to-end foundation model engineering: autoregressive pre-training datasets, byte-pair tokenization, causal self-attention, KV caching optimization, and decoding strategies (top-p, temperature).
Bridging vision and language: OpenAI CLIP contrastive pre-training, modality-specific encoders, cross-attention projection bottlenecks, and visual instruction tuning.
Frontier deep learning architectures: Mixture of Experts (MoE) sparse routing, top-k expert gating, load balancing auxiliary loss, Chinchilla scaling laws, and speculative decoding.