An autonomous navigation system for drones in both urban and rural environments.
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Updated
Jan 6, 2026 - C++
An autonomous navigation system for drones in both urban and rural environments.
Quantum MNIST using amplitude encoding instead of dimensionality reduction.
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
An MNIST dataset classifier implemented from scratch in NumPy.
This repo hold CV models for the Classification of single digit images. I used Pytorch and the Digit-Recognizer kaggle dataset for the training.
MNIST handwritten digit classification using PyTorch
Test project for neural networks - Handwritten digit recognition on MNIST dataset
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.
A bare-bones (minimal dependencies) implementation of some ML algorithms (classifying/clustering) as part of the Machine Learning postgraduate course assignments in the GUC
OCR for numbers in the MNIST dataset using various ML techniques.
All my machine learning projects and tests.
Machine Learning model that recognizes hand written digits
MNIST Classification with Convolutional Neural Networks
MNIST classifier using CNTK written in C++ and C#. Only used fully connected layers.
Artificial neural networks processed with Tensorflow
This project implements a CNN for handwritten digit classification on the MNIST dataset using PyTorch. It uses stacked convolutional layers with dropout, batch normalization, and max pooling to classify 28×28 grayscale digits (0–9) with Softmax output.
An MNIST number recognition created with Intel's OneAPI, compatible with Intel Arc GPUs, and potentially other devices.
Naive Implementation of PyTorch framework to solve the MNIST-Digit_Recognition Problem
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