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Initial commit: MLP regularization, CNN BatchNorm comparison, autoencoder denoising

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# Deep Learning Portfolio
A collection of deep learning experiments built with TensorFlow/Keras — regularization and normalization comparisons, and an autoencoder for image denoising.
![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white)
![Keras](https://img.shields.io/badge/Keras-D00000?style=flat&logo=keras&logoColor=white)
![Python](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white)
📖 **Full write-ups with result plots:** [docs/Home.md](docs/Home.md)
## Projects
| Project | Comparison | Result |
|---|---|---|
| [mnist-mlp-regularization](mnist-mlp-regularization/) | Baseline MLP vs Dropout | Test loss 0.279 → 0.099 *(not a clean ablation — see project README)* |
| [cnn-batchnorm-comparison](cnn-batchnorm-comparison/) | Baseline CNN vs BatchNorm | Test loss 0.289 → 0.094 (clean ablation) |
| [autoencoder-denoising](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](https://github.com/rezayaghobi-dev)