***This is based on Kaggle's Digit Recognizer competition at [https://www.kaggle.com/competitions/digit-recognizer/overview](https://www.kaggle.com/competitions/digit-recognizer/overview). The labels of the test set from the Digit Recognizer competition are available widely and may be used by some as part of the training set, leading to perfect accuracy scores which skews the actual performance of the prediction models. This competition uses a near leakage-free test set that would provide a better assessment of the model performance on the Leaderboard. The initial test data set (source to be disclosed) likely contains out-of-distribution samples that shows potential biases in the MNIST data set. Updated test data set may be used before the end of the competition.***

The following is taken from the Kaggle competition description with minor changes.

### Competition Description

MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We’ve curated a set of tutorial-style kernels which cover everything from regression to neural networks. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.
Practice Skills

-     Computer vision fundamentals including simple neural networks
-     Classification methods such as SVM and K-nearest neighbors

### Acknowledgements 

More details about the dataset, including algorithms that have been tried on it and their levels of success, can be found at [http://yann.lecun.com/exdb/mnist/index.html](http://yann.lecun.com/exdb/mnist/index.html). The dataset is made available under a [Creative Commons Attribution-Share Alike 3.0 license](https://creativecommons.org/licenses/by-sa/3.0/).