I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
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Updated
Nov 21, 2017 - Jupyter Notebook
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
MNIST handwritten digit classification using PyTorch
Problems Identification: This project involves the implementation of efficient and effective KNN 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.
MNIST classification using scikit-learn
Hand written digits recognition using simple gradient descent on MNIST data
Test project for neural networks - Handwritten digit recognition on MNIST dataset
Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth
OCR for numbers in the MNIST dataset using various ML techniques.
An implementation of the MNIST neural network for use in backend ML systems.
All my machine learning projects and tests.
Feed forward neural network using Numpy for MNIST classification.
An autonomous navigation system for drones in both urban and rural environments.
Naive Implementation of PyTorch framework to solve the MNIST-Digit_Recognition Problem
NumPy-based feed-forward neural network
An MNIST dataset classifier implemented from scratch in NumPy.
This repository includes a study that aims to apply classification on well-known MNIST dataset. Detailed info in ReadMe
MNIST classifier with a graphical user interface and a canvas for drawing the digits, doing classifying in real time
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