Neural Networks

This repository contains assignments and projects related to Neural Networks. It includes a series of Jupyter Notebooks with practical applications and demonstrations of various machine learning techniques, particularly focusing on neural networks. The repository also contains datasets for classification tasks used in the assignments.

Table of Contents

• Assignments
• Datasets
• Technologies Used
• Usage
• Contributing
• License

Assignments

This repository includes the following assignments:

1. Assignment1.ipynb - Basic neural network implementation using a simple dataset for classification.
2. Assignment2.ipynb - Implementing a deeper neural network model and analyzing its performance.
3. Assignment3.ipynb - Exploring advanced techniques like regularization, optimization, and hyperparameter tuning.
4. Final project.ipynb - A final project integrating all the concepts learned, demonstrating a real-world application of neural networks.
5. A4.ipynb - An additional assignment exploring more complex neural network architectures.

Datasets

The following datasets are used in the notebooks for classification tasks:

• breast-cancer-wisconsin.data.csv: A dataset used for classifying breast cancer instances into malignant or benign.
• glass.data.csv: A dataset used for classifying types of glass based on chemical analysis and refractive indices.

Technologies Used

• Python 3.x
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• TensorFlow / Keras (for deep learning models)

Usage

To use this repository, follow these steps:

1. Clone the repository to your local machine:

   git clone https://github.com/Yashasvi1714/NeuralNetworks.git