This repository contains the code to reproduce the blog post on regularization in deep learning, available at https://tomkite.dev/posts/regularisation/index.html. The blog post explores the importance of regularization both experimentally and theoretically, providing insights into various techniques and their applications in neural networks.
- high_dim_linear.ipynb: A Jupyter notebook demonstrating the effects of different regularization techniques in high-dimensional linear models.
- utils.py: A Python script containing utility functions used in the notebook for data processing, model evaluation, and visualization.
To run the code in this repository, you need the following dependencies:
- Python 3.x
- Jupyter Notebook
- NumPy
- SciPy
- Matplotlib
- scikit-learn
- Any other dependencies listed in the respective files