DL-portfolio/README.md

Deep Learning Portfolio

A collection of deep learning experiments built with TensorFlow/Keras — regularization and normalization comparisons, and an autoencoder for image denoising.

📖 Full write-ups with result plots: docs/Home.md

Projects

Project	Comparison	Result
mnist-mlp-regularization	Baseline MLP vs Dropout	Test loss 0.279 → 0.099 (not a clean ablation — see project README)
cnn-batchnorm-comparison	Baseline CNN vs BatchNorm	Test loss 0.289 → 0.094 (clean ablation)
autoencoder-denoising	Noisy → clean image reconstruction	Recognizable digits recovered through a 32-unit bottleneck

Each project folder has its own README with architecture, training config, and results. The docs/ folder has deeper write-ups with the actual result plots.

Tech stack

Python, TensorFlow, Keras, NumPy, matplotlib

Author

Reza — Computer Networks student, LPIC-1 GitHub