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
Implementing CNN for Digit Recognition (MNIST and SVHN dataset) using PyTorch C++ API
Handwritten digit recognition implemented in c++ without libraries
Multi-layered Convolutional Neural Network written in C++11
Shallow Neural Network implemented using C++ that learns how to classify handwritten digits with 84.42% precision using the MNIST dataset.
Neural Network for recognition of handwritten digits.
Handwritten digit recognition using simple neural net
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset
Include c++ code which extract MNIST handwritten digit images from binary into OpenCV mat type.
Comparison of multiple methods for calculating MNIST hand-written digits similarity.
C++/CUDA libary for neural networks
Interactive hand-drawn number image recognition classifier.
Machine Learning Basics in C++
Autoencoder dimensionality reduction, EMD-Manhattan metrics comparison and classifier based clustering on MNIST dataset.
Searching similar images (represented as vectors from MNIST dataset) and clustering on them.
First assignment for the University Senior Project course
A simple handwritten digit classifier NN implemented from scratch in C++.
From scratch C++ Neural Network based on MNIST dataset using templated Tensors with SIMD intrinsics
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