Structure of MNIST dataset
The MNIST dataset is a collection of 70,000 handwritten digits (0-9), with each image being 28×28 pixels. Here is the dataset information in the specified format:
- Number of Instances: 70,000 images
- Number of Attributes: 784 (28×28 pixels)
- Target: Column represents the digit (0-9) corresponding to the handwritten image
- Pixel 1-784: Each pixel value (0-255) represents the grayscale intensity of the corresponding pixel in the image.
- The dataset is divided into two main subsets:
- Training Set: Consists of 60,000 images along with their labels, commonly used for training machine learning models.
- Test Set: Contains 10,000 images with their corresponding labels, used for evaluating the performance of trained models.
MNIST Dataset : Practical Applications Using Keras and PyTorch
The MNIST dataset is a popular dataset used for training and testing in the field of machine learning for handwritten digit recognition. The article aims to explore the MNIST dataset, its characteristics and its significance in machine learning.
Table of Content
- What is MNIST Dataset?
- Structure of MNIST dataset
- Origin of the MNIST Dataset
- Methods to load MNIST dataset in Python
- Loading MNIST dataset using TensorFlow/Keras
- Loading MNIST dataset Using PyTorch
- Significance of MNIST in Machine Learning
- Applications of MNIST