Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
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Updated
Nov 17, 2022 - Java
Android TensorFlow MachineLearning MNIST Example (Building Model with TensorFlow for Android)
A resource-conscious neural network implementation for MCUs
A collection of codes for 'how far can we go with MNIST' challenge
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
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
Some basic implementations of Variational Autoencoders in pytorch
This repo contains all the necessary files to build a MNIST TinyML application, that works with an OV7670 camera module and TFT LCD module.
Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Building a model to recognise handwritten numerical digits from images of the MNIST dataset.
Performs OCR on the MNIST dataset. From my BSc. AI & Robotics at Prifysgol Aberystwyth
Deep learning demos using MNIST data set with multiple neural network models
Deep Neural Networks like Single Layer Perceptron and Multi Layer Perceptron implementation using Tensorflow library on Datasets like MNIST and Naval Mine for categorical Classification. Saving and Restoring Tensorflow "Variables" weights for testing.
Simple NN for MNIST Recognition
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
This is a DCGAN trained on MNIST model. It has all the specifications as described the original paper on Deep Convolutional General Adversarial Training
VAE Implementation with LSTM Encoder and CNN Decoder
Short python jupyter script, for training Deep learning model for MNIST Dataset about Numbers classification from images and it's evaluation.
A Convolutional neural network heavily based upon the tensorflow advanced MNIST example but equiped with labels to visualize and allowing the user to draw an image and then have the system predict the result.
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