Origin of the MNIST Dataset
The MNIST dataset, which currently represents a primary input for many tasks in image processing and machine learning, can be traced back to the National Institute of Standards and Technology (NIST). NIST, a US government agency focused on measurement science and standards, curates various datasets, including two particularly relevant to handwritten digits:
- Special Database 1 (SD-1): Since being the Bureau of US census employees with sizable population among the workplace was private handwritten data – they all came from a desirable source. Census staff are seen handling written values on a repeat basis, thus rendering their samples a high chance of success in algorithm training.
- Special Database 3 (SD-3): This data set contained digitized handwriting figures of high-schoolers, provided by students. However, in terms of authenticity, this information looked less “official” than the numbers provided by the Census Bureau, but the great thing is that they applied in a variety of writing styles.
While these datasets existed, unfortunately, they could not be used directly and instead, they had to be transformed and divided into specifically data for training and testing the AI models. The separation between the two NIST collections created a potential bias:
- SD-1 was then kept aside as a teaching set. The AI problem can be attributed to the fact that the technicians having more experience in writing the hand-written numbers. So the model might go on to become overly biased towards such “clean” numbers.
- In SD-3 we assigned it to do the test runs. Without being exposed to more types of write styles during training (if only from SD-1), the model may misguided on SD-3 testing.
To tackle this bias and get a more balanced data set for machine learning, the MNIST developers used an original trick of combining characters from NIST Special databases and symbols from a such font as Zapf Dingbats. By using this approach, the data used for both training and testing became more inclusive of the wide range of alphabets used, thereby resulting in more generally applicable data processing and machine learning 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