This image shows examples of handwritten digits from the MNIST dataset. Each digit is a 28 x 28 grayscale image, and the dataset includes a total of 70,000 such images. The goal of a machine learning model trained on this dataset would be to accurately classify each image based on the digit it represents.
This repository contains code and resources for working with neural networks and the MNIST dataset. The MNIST dataset is a widely used benchmark dataset in machine learning, consisting of 70,000 handwritten digit images, each 28 x 28 pixels in size. The goal of the dataset is to train a neural network to accurately classify the digits from 0 to 9.
To get started with this project, follow these steps:
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Clone the repository to your local machine using
git clone https://github.com/wiiggapony0925/MNIST_Dataset-.git. -
Install any necessary dependencies. The code in this repository requires Python 3 and several Python packages, including NumPy, TensorFlow, and Keras.
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Explore the code and resources in the repository. This repository includes several Jupyter notebooks that demonstrate how to load and preprocess the MNIST dataset, build and train a neural network, and evaluate the model's performance.
Here are some resources to help you get started with working with neural networks and the MNIST dataset:
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MNIST database: The official website for the MNIST dataset, including links to download the dataset and information on benchmark results.
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Keras documentation: The official documentation for Keras, a high-level neural networks API, which is used extensively in this repository.
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TensorFlow documentation: The official documentation for TensorFlow, a popular open-source library for building and training machine learning models.
If you find a bug or would like to suggest a new feature, please open an issue or submit a pull request. We welcome contributions from the community and appreciate your help in improving this repository.
This repository is licensed under the MIT License. See the LICENSE file for more details.
This repository was inspired by the work of many researchers and practitioners in the field of machine learning, and we are grateful for their contributions to this field. We also thank the developers of the MNIST dataset, Keras, and TensorFlow for their open-source contributions to the machine learning community.