https://github.com/akshint0407/ann-lab
Made this file to take the futures Engineer's a load off their shoulders😉. Feel free to use these and acknowledge me in the process. Happy Learning! 😌
https://github.com/akshint0407/ann-lab
3rd-year-2nd-semester ann artficial-neural-network artificial-intelligence-and-data-science engineering experiments practicals python
Last synced: about 2 months ago
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Made this file to take the futures Engineer's a load off their shoulders😉. Feel free to use these and acknowledge me in the process. Happy Learning! 😌
- Host: GitHub
- URL: https://github.com/akshint0407/ann-lab
- Owner: Akshint0407
- License: mit
- Created: 2025-04-12T17:39:05.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-05-05T02:16:13.000Z (5 months ago)
- Last Synced: 2025-07-20T07:21:37.919Z (3 months ago)
- Topics: 3rd-year-2nd-semester, ann, artficial-neural-network, artificial-intelligence-and-data-science, engineering, experiments, practicals, python
- Language: Jupyter Notebook
- Homepage:
- Size: 2.05 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Artificial Neural Networks (ANN) Lab Experiments
This repository contains 10 core Artificial Neural Networks (ANN) experiments performed as part of the 3rd-year AIDS curriculum. The goal is to provide a structured and beginner-friendly reference for students looking to understand and implement foundational ANN concepts using Python and relevant libraries.
## About the Repository
Each experiment folder contains:
- Source code (with comments)
- Sample output screenshots (if available)
- Brief explanations and instructions to run the code
The experiments cover a variety of ANN topics including perceptrons, backpropagation, activation functions, and practical model training using datasets.
---
## List of Experiments
1. **Perceptron Algorithm Implementation**
2. **Backpropagation Algorithm**
3. **McCulloch-Pitts Neuron Model**
4. **AND, OR, XOR Gate using Neural Networks**
5. **Activation Functions (Sigmoid, Tanh, ReLU)**
6. **Gradient Descent Implementation**
7. **Feedforward Neural Network**
8. **ANN for Classification (Iris Dataset)**
9. **ANN for Regression (Custom Dataset)**
10. **MNIST Digit Recognition using ANN**
---
## How to Use
1. Clone the repository:
```bash
git clone https://github.com/Akshint0407/ANN-Lab-Experiments.git
2. Make sure Jupyter Notebook is installed. If not, install it using:
```bash
pip install notebook
```
3. Launch Jupyter Notebook:
```bash
jupyter notebook
```
4. Navigate to the experiment folder and open the .ipynb file of your choice.
Ensure required libraries like NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow/Keras are installed in your environment.
## Target Audience
- 3rd Year AIDS Students
- Beginners in Neural Networks
- Anyone exploring foundational AI/ML concepts
## Contributions
If you have improvements, bug fixes, or additional experiment ideas, feel free to fork this repo and raise a pull request!
## License
This project is licensed under the [MIT License](License).
## Connect with Me
Akshint
[Linkedin](linkedin.com/in/Akshint-Varma)• [GitHub](github.com/Akshint0407)
***Feel free to share with your classmates and juniors. Happy Learning!***