A comprehensive repository containing lab tasks and solutions for Artificial Neural Networks (ANN). Designed to facilitate learning and application of ANN concepts through practical tasks and examples.
This repository serves as a resource for students and enthusiasts interested in exploring and understanding Artificial Neural Networks (ANN) through hands-on lab tasks and solutions. It features a collection of 8 Word documents detailing lab tasks, accompanied by 8 Jupyter Notebook files providing detailed solutions and explanations.
The repository is organized into two main folders:
Lab Tasks: Contains 8 Word documents, each outlining a specific lab task designed to challenge and enhance your understanding of ANN concepts.Lab Solved: Comprises 8 Jupyter Notebook files (*.ipynb), each corresponding to a lab task and providing a comprehensive solution with code, explanations, and visualizations.
To get started with the lab tasks and solutions, follow these steps:
- Clone this repository to your local machine using
git clone <repository-url>. - Ensure you have Jupyter Notebook installed on your system. If not, you can install it via Anaconda or by running
pip install notebook. - Navigate to the
Lab Solvedfolder and open the Jupyter Notebook files to view the solutions.
- Python 3.x
- Jupyter Notebook
- Necessary Python libraries as specified in the notebooks (e.g.,
numpy,matplotlib,tensorflow).
To make the most out of this repository:
- Start by reviewing the lab tasks in the
Lab Tasksfolder. - Attempt to solve the tasks on your own to reinforce your learning.
- Refer to the corresponding solution in the
Lab Solvedfolder for guidance, detailed explanations, and code insights.
We welcome contributions to enhance the tasks and solutions or to add new resources. Please refer to the CONTRIBUTING.md file for contribution guidelines.
For any queries or collaboration, feel free to contact me:
- Email: [OWD.1@outlook.com]