Deep Learning Engineering

Production deep learning systems from architecture selection through deployment optimization. We build neural networks that perform reliably in real-world conditions with proper evaluation and uncertainty quantification.

Deep Learning Expertise

Neural Architecture Design

Selection and customization of CNN, transformer, and hybrid architectures for specific tasks. Experience with ResNet, EfficientNet, Swin Transformer, DeiT, and custom architectures combining multiple approaches for optimal performance.

Training Pipeline Development

End-to-end training infrastructure including data pipelines, augmentation strategies, distributed training, checkpointing, and monitoring. Built to handle large datasets and multi-GPU environments efficiently.

Federated Learning

Privacy-preserving deep learning across distributed data sources. Implemented FedAvg, FedProx, FedBN, SCAFFOLD, and MOON for medical datasets where data cannot be centralized. Handles non-IID client distributions and communication constraints.

Parameter-Efficient Fine-Tuning

LoRA and QLoRA implementation for adapting large models with limited resources. HEC-funded research on multi-agent LLM systems using efficient fine-tuning methods for task-specific adaptation.

Model Optimization & Compression

Quantization, pruning, knowledge distillation, and mobile deployment optimization. Delivered face liveness detection on mobile devices with acceptable latency and resource constraints.

Evaluation & Benchmarking

Multi-seed evaluation protocols, calibration analysis, cross-dataset validation, and ablation studies. Published work includes systematic comparison of architectures with reproducible results and statistical confidence intervals.

Research & Production Record

  • Published Frontiers in Neuroscience 2026: Calibration-aware CNN-Swin model for brain MRI
  • 99.55% accuracy on medical image classification with proper uncertainty quantification
  • Federated learning research across non-IID cervical cell classification datasets
  • HEC-funded LoRA and multi-agent LLM research project
  • Production deployment: face liveness detection system with 40,000+ verifications
  • Accepted ICoDT2 2025 poster on LLM-based assessment generation with fine-tuning

Frequently Asked Questions

What deep learning services does Kortex8Labs offer?
We provide neural network architecture design, training pipeline development, federated learning implementation, model fine-tuning with LoRA/QLoRA, hyperparameter optimization, and production deployment strategies.
What frameworks does Kortex8Labs work with?
We work primarily with PyTorch and TensorFlow for deep learning development, with experience in Hugging Face transformers, PEFT libraries for efficient fine-tuning, and federated learning frameworks.
Can Kortex8Labs implement federated learning?
Yes. We have research experience implementing FedAvg, FedProx, FedBN, SCAFFOLD, and MOON algorithms for privacy-preserving learning across distributed medical datasets with non-IID client distributions.
How does Kortex8Labs optimize model performance?
We use multi-seed evaluation for reproducibility, hyperparameter tuning, architecture search where appropriate, model calibration for uncertainty, and ensemble methods. Our brain MRI work achieved 99.55% accuracy through systematic optimization.
What is the typical timeline for deep learning projects?
Timelines depend on problem complexity, data availability, and performance requirements. We start with focused discovery (1-3 weeks) to assess feasibility, then proceed with milestone-based development including baseline models, optimization iterations, and production preparation.

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