https://github.com/chaitanyak77/handwriting-classification-of-mnist-dataset-advanced-

https://github.com/chaitanyak77/handwriting-classification-of-mnist-dataset-advanced-

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• Host: GitHub
• URL: https://github.com/chaitanyak77/handwriting-classification-of-mnist-dataset-advanced-
• Owner: ChaitanyaK77
• License: mit
• Created: 2023-08-27T12:56:50.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-27T13:00:02.000Z (over 1 year ago)
• Last Synced: 2023-09-06T08:28:37.549Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 44.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Handwriting Classification of MNIST dataset (ADVANCED)

Welcome to the MNIST Handwritten Digit Classification project! In this endeavor, we delve into the fascinating realm of machine learning and image analysis to tackle the task of recognizing handwritten digits using the iconic MNIST dataset. Our overarching objective is to harness the power of various machine learning algorithms and techniques to construct models capable of accurately distinguishing between digits from 0 to 9, regardless of the diverse styles in which they are handwritten. The MNIST dataset, consisting of a whopping 60,000 training images and an additional 10,000 testing images, serves as the foundation for our exploration.

Within this repository, you'll discover an array of code files and Jupyter notebooks designed to take you on a guided tour through our journey. From meticulous data preprocessing strategies to intricate model training sessions, and culminating in thorough evaluation methodologies, the notebook presents a distinct chapter in our narrative. The MNIST dataset, curated by the experts at http://yann.lecun.com/exdb/mnist/, serves as a benchmark in the field of digit recognition, enabling us to benchmark our models' performance against a well-established standard. I invite you to dive into my Jupyter notebooks, unravel the intricacies of digit classification, and embark on a learning adventure at the intersection of machine intelligence and human expression.