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

Star 62

🏆 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 Nov 22, 2024
  • Python

navjindervirdee / neural-networks

Star 38

Implemented Convolutional Neural Network, LSTM Neural Network, and Neural Network From Scratch in Python Language.

python deep-learning numpy jupyter-notebook cnn neural-networks convolutional-layers rnn derivatives scratch matplotlib convolutional-neural-networks handwritten-digit-recognition lstm-neural-networks iris-dataset lstm-cells lstm-networks backward-propagation forward-propagation backward-propagation-through-time
  • Updated Sep 19, 2018
  • Jupyter Notebook

deyjishnu / digit-recognition

Star 33

The purpose of this project is to take handwritten digits as input, process the digits, train the neural network algorithm with the processed data, to recognize the pattern and successfully identify the test digits. The popular MNIST dataset is used for the training and testing purposes. The IDE used is MATLAB

machine-learning matlab artificial-intelligence mnist neural-networks handwritten-digit-recognition final-year-project
  • Updated Apr 2, 2019
  • MATLAB

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