GitHub4y
Recognizing MNIST digits. Simple 10-class classification problem, largely solved in the vision community, but I just wanted to experiment with it.
To download and train the model to replicate this code, it's a very simple two-line approach. It is important to note this code requires at least PyTorch 1.3.1 and Torchvision 0.4.2, as the model ...
GitHub3y
Handwritten digits recognition trained with augmented MNIST and USPS dataset.
2, Convert the MNIST dataset to images. python3 convert_mnist.py 3, Check the datasets and remove the abnormal digits mannualy, which will harm the accuracy of the results. 4, Convert the USPS images ...
Hackaday1mon
Going Digital: Teaching A TI-84 Handwriting Recognition
The innovative project involved running a neural network directly on a TI-84 Plus CE to recognize handwritten digits. By using the MNIST dataset, a well-known collection of handwritten numbers ...
Tech Xplore on MSN6mon
A new neural network makes decisions like a human would
The team trained their neural network on handwritten digits from a famous computer science dataset called MNIST and asked it ...
Hackaday1mon
texas instruments
The innovative project involved running a neural network directly on a TI-84 Plus CE to recognize handwritten digits. By using the MNIST dataset, a well-known collection of handwritten numbers ...
IEEE4y
Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
Abstract: Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark. All we need ...
IEEE1y
Gyoo-Soo Chae
Images Of Handwritten Digits,Loop Antenna,MNIST Dataset,Mean Filter,Mean Square Error,Median Filter,Mobile System,Noise Injection,Noise Scenarios,Normalized Mean Square,Normalized Mean Square ...