{"id":24594489,"url":"https://github.com/mohammedsaqibms/regularization","last_synced_at":"2025-03-18T04:28:46.053Z","repository":{"id":256614637,"uuid":"855934612","full_name":"MohammedSaqibMS/Regularization","owner":"MohammedSaqibMS","description":"This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes forward/backward propagation, cost functions, and decision boundary visualization. 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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.\n\nLet's dive into the project!\n\n## 🧠 Introduction to Regularization\n\nRegularization 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:\n\n- **L2 Regularization:** Adds a penalty proportional to the squared value of the weights, which discourages large weight values.\n- **Dropout Regularization:** Randomly turns off a fraction of neurons during training to prevent the network from becoming too reliant on specific neurons.\n\n## 🔗 Acknowledgements\n\nThis project was developed as part of the **Deep Learning Specialization** by [DeepLearning.AI](https://www.deeplearning.ai/courses/deep-learning-specialization/). Special thanks to their incredible team for providing the foundational content.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammedsaqibms%2Fregularization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohammedsaqibms%2Fregularization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammedsaqibms%2Fregularization/lists"}