Some of Machine Learning work
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Updated
Feb 18, 2020 - Jupyter Notebook
Some of Machine Learning work
Handwritten Digit Recognition Using Random Forest
This repository contains codes for recognising handwritten digits using neural networks with the help of python and tensorflow and MNIST dataset.
Fully Connected Deep Network for MNIST dataset written as part of my MSc in Data Science degree. Made in Numpy/Cupy from scratch.
It is a nueral network built using Keras for recogonising handwritten digits. The net was trained on MNIST dataset and achieved 99.3% accuracy.
Understanding the performance of different neural network architectures on the MNIST handwritten digits dataset, implemented in Tensorflow.
based on Andrew Ng course
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.
NumPy-based feed-forward neural network
classification of handwritten digits from MNIST dataset with above 99% accuracy using simple CNNs
A Simple application to recongize digit using Tenorflow.js in Node.js
Keras MNIST dataset predict handwritten digits from web url, local pc or from passing an argument. With Graphical Representation using Matplotlib.
Codes of Deep Learning Models
Using Deep Learning in the form of a Convolutional Neural Network (CNN) to classify images of different digits!
A machine learning program that lets you draw a digit and recognises it with high accuracy. This uses Convolutional Neural Networks.
The MNIST database of handwritten digits, available from (http://yann.lecun.com/exdb/mnist/), has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.<br> It is a good database for people who want to try l…
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