Bayesian Learning Web Apps!
Published:
I’ve developed my first web-hosted app that features a Bayesian neural network using convolution (think computer-vision) to classifies handwritten digits from 0 to 9. The neural network was trained on the MNIST dataset using image augmentation such as random image translations and rotations to better generalize the model to read-world never-seen handwritten digits. There is a sidebar that can be opened with the little arrow in the top left of the web window. There you can select how many re-samplings you want the model to perform to estimate probabilities for your handwritten digit. Once you draw a number, press submit and the model will output it’s predictions. After the model finishes predicting it outputs its probabilities for each possible digit in a bar graph below the canvas. There you can see if the model is very (or not) certain about what number you wrote. In the sidebar is a checkbox to keep track of how many correct (and incorrect) predictions the model gets for you. The next update is aimed to adding OpenCV code to perform multi-digit separation of people’s input to be able to recognize multi-digit numbers. Hope you enjoy!