Handwritten_Digit_Classification

1. Introduction: A brief description of the MNIST dataset, including its purpose (handwritten digit recognition). Mention that it is a widely used benchmark dataset in machine learning.

2. Dataset Contents: Images: 70,000 grayscale images of handwritten digits (0-9). 60,000 for training. 10,000 for testing. Image Specifications: Size: 28x28 pixels. Format: Grayscale, with pixel values typically ranging from 0 to 255. Preprocessing: Images are typically size-normalized and centered. Labels: Corresponding labels for each image, indicating the digit it represents.

3. Data Format: Explanation of how the data is stored (e.g., in a specific file format like IDX, or as CSV files). If provided in CSV format, describe the structure of each row (e.g., first value is the label, subsequent values are pixel data).

4. How to Use: Instructions on how to load and access the dataset, often including code snippets in popular languages like Python (using libraries like NumPy, TensorFlow, or Keras).