This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting.
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Updated
Jul 2, 2024 - Lua
This research aims to enhance the performance of LBP-based convolutional neural networks on the automatic recognition of bilingual handwriting.
This project encompasses a series of modules designed to facilitate the creation, training, and prediction using a PyTorch MLP Neural Network for digit classification based on the MNIST dataset.
This is a machine learning project focused on accurately classifying handwritten digits from the popular MNIST dataset. It provides a collection of code and models that leverage different techniques to achieve high accuracy in recognizing and categorizing digits.
An implementation of a multilayer perceptron in python using numpy. Trained to achieve 96.14% on the MNIST dataset
Examples of Tensorflow Lite on Android
Training different models to solve the MNIST digit dataset
An implementation of multiclass perceptron as a digit recognizer
Sample android application to detect digit drawn using TensorFlow & Firebase model.
An implementation showcasing the deployment of machine learning model onto the flask server with live demo deployed on AWS Lambda.
A simple digit classifier built using the MNIST dataset
Machine learner to classify a single handwritten digit on an image.
Real time digit classifier using python and Deep neural network.
MATLAB digit classifier based on Andrew NG course
It is a Python GUI in which you can draw a digit and the ML Algorithm will recognize what digit it is. We have used Mnist dataset
In this project, I use Keras and TensorFlow to classify digits and python's Tkinter library to visualize
A digit-classifier using CNN
2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
A Machine Learning project that uses EM and Bernoulli mixes to classify digits
A handwritten digit classifier application with python, java, klotin and tensorflow dependencies
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