A resource-conscious neural network implementation for MCUs
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Updated
May 17, 2024 - C++
A resource-conscious neural network implementation for MCUs
I used expert mode of TensorFlow to solve some problems
Amos optimizer with JEstimator lib.
A handwritten digit recognition machine learning model made in rust and trained with the mnist dataset
A Convolutional Neural Network from scratch in python, using no ML libraries, only numpy and scipy
[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification
MNIST classification using kNN and Neural Networks
Data and code for our analysis of DermaMNIST (MedMNIST), HAM10000, and Fitzpatrick17k datasets
MLE, MAP, Bayesian Inference and Variational Autoencoders using MNIST datasset
Introduction to Python and Neural Network with Keras and PyTorch for beginners
A zip file containing images for MNIST-M dataset
improved vanilla GAN model. it's probably overfitting
🤖 My first neural net in Pytorch, for MNIST (handwritten number recognition)
Collection of Machine Learning Algorithms
Handwritten Digit Classification on MNIST Dataset, Utilising Only Traditional Machine Learning Techniques
Python toolkit for speech processing
vanilla GAN on the MNIST dataset. There is room for improvement in terms of generator's architecture and training
🦀 Neural Network for Handwritten Digit Recognition, written from Scratch in Rust
The main goal of the project was to develop a model that accuratelyclassifies handwritten digits. I trained the model using the MNISTdataset and the CNN algorithm.. Later on, I integrated this model with image processingtechniques to create a project that recognizes digits in real-time
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