Multi-Class Classification with Logistic Regression and Neural Networks

Developed Model to detect handwritten digits. Model is trained on MNIST data set, and 10 clusters are achieved as expected.

This repository contains codes for recognising handwritten digits using neural networks with the help of python and tensorflow and MNIST dataset.

Training a LeNet Model proposed by Yenn Lecunn to train on MNIST Handwritten Digits dataset

Fully Connected Deep Network for MNIST dataset written as part of my MSc in Data Science degree. Made in Numpy/Cupy from scratch.

some basics of DL

Android App thats used to detect hand written digits using MNIST dataset.Takes input as BitMap , tested on MNIST .tflite model and outputs the result in form of BitMap

It is a nueral network built using Keras for recogonising handwritten digits. The net was trained on MNIST dataset and achieved 99.3% accuracy.

A simple approach to develop a handwritten digit classification model using MNIST dataset with Deep Learning

Classification / identification of Hand written numerals from MNIST image dataset using Artificial Neural Networks

Homework Assignments Completed as a part of the course on "Foundations of Artificial Intelligence" at USC.

Understanding the performance of different neural network architectures on the MNIST handwritten digits dataset, implemented in Tensorflow.

based on Andrew Ng course

Handwritten digits recognition written in C

It reads handwritten numbers given an input of pixel values. A supervised learning, gradient descent, mini-batching, softmax-output-activation neural network that is meant to be trained on the MNIST dataset (dataset not included in this repository).

A NN for MNIST handwritten digit classification using TensorFlow and PyTorch.

Hand written digit recognition using neural networks

Simple ML app using Gradio and Tensorflow to identify handwritten digits

Projects developed during the Artificial Intelligence course at college.