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.
Computer vision fundamentals including simple neural networks
Classification methods such as SVM and K-nearest neighbors
How to use
- Clone the repository
git clone https://github.com/nunesdaniel/digit_recognizer
If necessary download the dataset MNIST and move the files
train.csvandtest.csvto pathdata
-
Install the dependencies
pip install -r requeriments.txt -
Run the file
cnn.py
Results
The shape of dataset
Data sample
Summary
Results for class
Plots
Predictions
This project is under license MIT. For more details visit LICENSE.
By Daniel Nunes 👋 danielnunesdc