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https://github.com/gawadx1/exploring-digits-with-nn
A guided journey into neural networks, showcasing their application in digit recognition using the MNIST dataset. From foundational concepts to advanced implementations, this project bridges theory and practice for learners and developers alike.
https://github.com/gawadx1/exploring-digits-with-nn
classification hyperparameter-tuning keras keras-neural-networks neural-networks tensorflow
Last synced: 4 days ago
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A guided journey into neural networks, showcasing their application in digit recognition using the MNIST dataset. From foundational concepts to advanced implementations, this project bridges theory and practice for learners and developers alike.
- Host: GitHub
- URL: https://github.com/gawadx1/exploring-digits-with-nn
- Owner: gawadx1
- License: mit
- Created: 2024-12-12T11:55:00.000Z (22 days ago)
- Default Branch: main
- Last Pushed: 2024-12-23T07:27:21.000Z (11 days ago)
- Last Synced: 2024-12-23T07:32:45.774Z (11 days ago)
- Topics: classification, hyperparameter-tuning, keras, keras-neural-networks, neural-networks, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 635 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# From Basics to Brilliance: Exploring Digits with Neural Networks
## Overview
This project is a comprehensive exploration of neural networks applied to digit recognition, designed to guide learners and developers from foundational concepts to advanced implementations. The notebook showcases practical implementations of machine learning techniques, leveraging the power of neural networks to classify handwritten digits.
## Features
- Step-by-step explanation of neural network fundamentals.
- Implementation of digit recognition models.
- Insights into training, validation, and testing workflows.
- Visualizations of data, training metrics, and results.
## Dataset
The project uses the **MNIST dataset**, a widely-used dataset for training and testing digit recognition models. It consists of 60,000 training images and 10,000 testing images of handwritten digits (0-9), each in a 28x28 grayscale format.
## Technologies Used
- **Python**: Programming language.
- **Jupyter Notebook**: Interactive environment for code, visualization, and explanations.
- **TensorFlow/Keras**: Frameworks for building and training neural networks.
- **Matplotlib/Seaborn**: Libraries for data visualization.
## Getting Started
### Prerequisites
Ensure you have the following installed:
- Python 3.8+
- Jupyter Notebook
- Required Python libraries: TensorFlow, NumPy, Matplotlib, Seaborn, Pandas, and Scikit-learn.
### Installation
1. Clone this repository:
```bash
git clone https://github.com/gawadx1/from-basics-to-brilliance
```
2. Navigate to the project directory:
```bash
cd from-basics-to-brilliance
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
### Usage
1. Open the notebook:
```bash
jupyter notebook from_basics_to_brilliance_exploring_digits_with_nn.ipynb
```
2. Follow the instructions in the notebook to execute the cells and explore the project.
### Results
The project demonstrates the effectiveness of neural networks in digit classification. It provides detailed performance metrics and visualizations for deeper insights.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.