Deep learning demos using MNIST data set with multiple neural network models
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Updated
May 16, 2017 - Python
Deep learning demos using MNIST data set with multiple neural network models
This repository contains jupyter notebooks explaining the basics of TF and deep learning classification model using TF
Practicing in Julia Lang
Classifying ANN for hand-written digit images from the MNIST data set
A simple API for C language created to work easier with MNIST data files.
Simple Neural Network application for the digit recognizer Kaggle challenge
In this project I worked on personalized handwritten datasets as well as standard MNIST dataset. I used deep learning Keras API
CNNs on mnist
Lasso regression for classification and salient feature identification.
Neural network from scratch
Text Digit Character Computer Vision using convolutional autoencoder
A simple study on the use of CNNs for a simple handwritten number image classification task using the Keras framework (with Tensorflow background).
Implemented and trained Siamese network for image classification using contrastive loss, triplet loss and regularized cross-entropy loss
Implemented a fully functioning Multi-Layer-Perceptron classifier for image classification and conducted various experiments using PyTorch
A Pytorch implementation of a customized LeNet-5 algorithm designed to give best results on the classic MNIST dataset.
A simple handwritten digit classifier NN implemented from scratch in C++.
Modular MLP in Python from scratch
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