Instrument

PyTorch

Level: 9/10 | Type: Deep Learning Framework & Tensor Acceleration | Ecosystem: Dynamic Computational Graphs, CUDA Kernels, TorchScript & PyTorch 2.x

9/10 level — dynamic computational graphs, CUDA GPU tensor acceleration, autograd, custom loss functions, mixed-precision inference, and zero-shot model execution.

Level: 9/10

PyTorchCUDANVIDIA GPUAutogradMixed Precision

Expert hands-on mastery in PyTorch (9/10). Designing, training, and serving neural network models on CUDA-accelerated hardware. Implementing memory-efficient inference pipelines, autograd mechanics, custom dataset loaders, tensor batching, model quantization, and distributed training primitives.

Where this has actually been used

Projects

Zero-Shot & Open-Vocabulary Visual Tracking Suite
NEED — Neuro-Evolution for Effective Decision
Tracker Failure Simulator — Visual Multi-Object Tracking & Evaluation Lab
NAVIROX — Autonomous Unmanned Vessel CV & 360° LiDAR Fusion POC
ResumeHR — Agentic Candidate Screening & Knowledge Distillation
Quantitative Algorithmic Trading & Reinforcement Learning Lab (PoC)

Publications

A Convolutional Neural Network-based Framework for the Assessment of Human Muscles

Experience

Technical Lead & Solutions Architect at QBS Co.

Products

VERSEYE

Loading content…