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mnist-classification-logistic

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🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

  • Updated Jan 30, 2025
  • Python

This project involves the implementation of efficient and effective Logistic Regression (FROM SCRATCH) classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.

  • Updated Jan 8, 2018
  • Jupyter Notebook

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