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mnist-classification-logistic

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harismuneer / Handwritten-Digits-Classification-Using-KNN-Multiclass_Perceptron-SVM

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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.

svm machine-learning-algorithms mnist-dataset logistic-regression support-vector-machines knn artificial-neural-network handwritten-digit-recognition k-nearest-neighbours supervised-machine-learning support-vector-classifier perceptron-learning-algorithm sigmoid-function delta-rule mnist-classification-logistic comparative-study multiclass-perceptron learning-algorithms-comparison accuracy-analysis efficiency-analysis
  • Updated Dec 4, 2024
  • Python

ibodumas / logistic_regression

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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.

machine-learning mnist-classification logistic-regression logistics logisticregression mnist-classification-logistic
  • Updated Jan 8, 2018
  • Jupyter Notebook

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