This project demonstrates a complete pipeline for recognizing handwritten digits using the MNIST dataset. The project is implemented in Python using Jupyter Notebook, and it covers data loading, preprocessing, model training, and performance evaluation of a Fully Connected Neural Network (FCNN).
The project uses the MNIST dataset, which consists of 70,000 images of handwritten digits (0-9) split into training and testing sets. The pipeline includes:
- Data Loading: Loading the MNIST dataset from a public source.
- Data Preprocessing: Normalizing and reshaping the images to be suitable for the FCNN model.
- Model Training: Building and training a Fully Connected Neural Network using TensorFlow/Keras.
- Performance Evaluation: Evaluating the model's accuracy and loss on the test set, and visualizing the results.
- Data Preprocessing: Techniques such as normalization and reshaping for optimal model performance.
- Model Architecture: Details of the FCNN layers, activation functions, and optimization techniques.
- Evaluation Metrics: Accuracy, loss, confusion matrix, and visualizations to assess the model's performance.
Hi, I'm Ahmad Ali, a passionate data scientist and machine learning enthusiast with a knack for solving complex problems using data-driven approaches. I have a strong background in data science and artificial intelligence, and I enjoy working on projects that involve deep learning, computer vision, and natural language processing.
- LinkedIn: https://www.linkedin.com/in/yourprofile
- Email: arsbussiness786@gmail.com