A resource-conscious neural network implementation for MCUs
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Updated
May 29, 2024 - C++
A resource-conscious neural network implementation for MCUs
PyTorch implementation of a feed forward neural network to classify handwritten digits from the MNIST dataset
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
This repo contains all the necessary files to build a MNIST TinyML application, that works with an OV7670 camera module and TFT LCD module.
VAE Implementation with LSTM Encoder and CNN Decoder
Trained deep neural networks to predict and classify input image (MNISTDD) datasets with python.
An Intuitive Desktop GUI Application For Recognizing Multiple Handwritten Digits Drawn At The Same Time. Trained On MNIST Dataset and Built With Python, OpenCV and TKinter
All of the code developed as part of my learning experience in the programming language R
Short python jupyter script, for training Deep learning model for MNIST Dataset about Numbers classification from images and it's evaluation.
This is a DCGAN trained on MNIST model. It has all the specifications as described the original paper on Deep Convolutional General Adversarial Training
MNIST Digits Classification with numpy only
Digit Recognition on MNIST Data
Some basic implementations of Variational Autoencoders in pytorch
Dockerize a Keras CNN model, which is wrapped in a Webapp using Flask Micro Framework
Played with Tensorspace a library for Neural network 3D visualization, building interactive and intuitive models in browsers, supports pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js
Building a model to recognise handwritten numerical digits from images of the MNIST dataset.
Study of leNet implementation in Python3.6 with Keras+Tensorflow backend.
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