🎯 Neural Network Regularization with L2 and Dropout

Welcome to this project on Regularization techniques in neural networks! In this repository, we implement and explore two key regularization methods: L2 Regularization and Dropout to improve model generalization and performance. Below, you'll find a detailed explanation of the code, along with its key components and results.

Let's dive into the project!

🧠 Introduction to Regularization

Regularization is essential for improving the generalization ability of machine learning models. It helps prevent overfitting, ensuring that the model performs well not only on the training data but also on unseen test data. In this project, we focus on:

• L2 Regularization: Adds a penalty proportional to the squared value of the weights, which discourages large weight values.
• Dropout Regularization: Randomly turns off a fraction of neurons during training to prevent the network from becoming too reliant on specific neurons.

🔗 Acknowledgements

This project was developed as part of the Deep Learning Specialization by DeepLearning.AI. Special thanks to their incredible team for providing the foundational content.