A resource-conscious neural network implementation for MCUs
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Updated
Jun 28, 2024 - C++
A resource-conscious neural network implementation for MCUs
Handwritten digit recognition implemented in c++ without libraries
Include c++ code which extract MNIST handwritten digit images from binary into OpenCV mat type.
From scratch C++ Neural Network based on MNIST dataset using templated Tensors with SIMD intrinsics
Neural Network for recognition of handwritten digits.
Shallow Neural Network implemented using C++ that learns how to classify handwritten digits with 84.42% precision using the MNIST dataset.
A mnist classifier in c++
A simple handwritten digit classifier NN implemented from scratch in C++.
Implementing CNN for Digit Recognition (MNIST and SVHN dataset) using PyTorch C++ API
Library to read the EMNIST and MNIST data sets
Extensible neural network class with backpropagation learning, tested on MNIST dataset
Comparison of multiple methods for calculating MNIST hand-written digits similarity.
First assignment for the University Senior Project course
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.
CUDA program for classifying MNIST-like images using a feed-forward network
Searching similar images (represented as vectors from MNIST dataset) and clustering on them.
Interactive hand-drawn number image recognition classifier.
Handwritten digit recognition using simple neural net
Multi-layered Convolutional Neural Network written in C++11
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