29 lines
1.3 KiB
Markdown
29 lines
1.3 KiB
Markdown
# 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](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)
|