Handwritten-digital-recognision-

In this project, I tackled the task of classifying handwritten digits from the MNIST dataset using deep learning techniques. First, I loaded the dataset and prepared it by resizing the images to a standard 28x28 pixel size and normalizing the pixel values for better model training. To handle the multi-class classification problem, I converted the digit labels into one-hot encoded vectors. For the heart of the project, I designed a Convolutional Neural Network (CNN) architecture using the Keras library. This CNN is well-suited for image classification tasks. The model consists of convolutional layers that learn to recognize patterns and features in the images, followed by pooling layers for down-sampling and flattening the data. Afterward, I added fully connected layers to make predictions. I trained the model on the preprocessed training data while monitoring its performance on a validation set to prevent overfitting. The training process involved optimizing the model using the categorical cross-entropy loss function and the Adam optimizer over 20 epochs. Once the training was complete, I evaluated the model on a separate test set to gauge its accuracy and generalization capabilities. This project essentially demonstrates the power of deep learning in recognizing handwritten digits, a fundamental task in the field of computer vision. The CNN architecture and training techniques used here can be applied to various image recognition tasks, offering a solid foundation for understanding and implementing neural networks for similar purposes.