01 / Foundation
Started with prediction systems
Built house price and insurance models to develop disciplined fundamentals around data and evaluation.
Building practical AI systems, one focused iteration at a time
Dhruv Garg
AI/ML Engineer in Training | Computer Vision + Generative AI
I build AI systems by staying consistent, experimental, and practical.
I build model-first prototypes in Python and PyTorch, then iterate toward deployable results. Most of my deep work happens in Colab through focused experiments, error analysis, and performance tuning.
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- 01 / Stage Undergraduate (Year 3)
- 02 / Strength Computer Vision + Generative AI
- 03 / Build Loop Experiment -> Evaluate -> Ship
- 04 / Current Goal AI Research / Engineering Internship
Trajectory
How I am evolving
Moving from ML baselines toward research-informed systems that are practical, reproducible, and useful.
02 / Vision
Moved into deeper CV work
Explored depth estimation and representation learning, then expanded into generation with GAN experiments.
Index
Selected Works
A mix of predictive ML, computer vision, and generative experiments that show how my work is evolving.
Real-time neural style transfer app optimized with perceptual loss and feed-forward transformer design.
- PyTorch
- Gradio
- VGG-16
Core
Capabilities
Strong ML foundations, disciplined experimentation, and gradual expansion into full-stack, cloud, and DevOps.
Vision Intelligence
- PyTorch and Torchvision
- Object Detection and Segmentation
- Multi-Modal Feature Fusion
AI Engineering
- Model Productization
- CLI and SDK Development
- Containerized Delivery
Technology Stack
Collaborate
I am looking for meaningful AI internships and research work.
If your team values consistency, curiosity, and practical ML execution, I would love to contribute. Email is the fastest way to reach me.