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knh4abt/README.md

Hi, I'm Nael

I build ML systems for the places they actually have to ship - cars, factories, clinics.

Mechatronics and Cyber Physical Systems MSc, Bosch ADAS background. Based in Stuttgart.

I care more about why a model fails than how well it scores.

  • driving-scene-segmentation - DeepLabV3+ vs SegFormer-B0 on Cityscapes. Where the two models disagree and why: truck/car confusion, the rider-vehicle boundary.
  • medvision - EfficientNet-B0 for colorectal cancer histology classification (99.6% val accuracy).
  • rul-for-nasa-jet-engines - Predictive maintenance on NASA C-MAPSS turbofan data.

Get in contact > nael.tarek.95@gmail.com LinkedIn > https://www.linkedin.com/in/nael-k-8016a711a/

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  1. driving-scene-segmentation driving-scene-segmentation Public

    Semantic segmentation for autonomous driving: DeepLabV3+ vs. SegFormer-B0 on Cityscapes. 72.9% vs. 60.1% mIoU — the gap is not noise, it's truck→car confusion at 26%. PyTorch · ADAS safety analysis…

    Python

  2. medvision medvision Public

    Histology tissue classification using EfficientNet and PyTorch

    Python

  3. rul-for-nasa-jet-engines rul-for-nasa-jet-engines Public

    End-to-end RUL prediction for jet engines using NASA CMAPSS data, designed as a modular, reproducible ML pipeline with software-engineering structure.

    Python