Using the MNIST data set, a collection of 70,000 handwriting samples of numbers 0-9, we predict which number each handwritten image represents using TENSORFLOW(with multi-level perceptrons).
Using the MNIST data set, a collection of 70,000 handwriting samples of numbers 0-9, we predict which number each handwritten image represents using a Convolutional Neural Network that's better suited for image processing. CNN's are less sensitive to where in the image the pattern is that we're looking for.
Using the ResNet50 model, trained on the imagenet data set, in order to quickly classify new images. The ResNet50 pre-trained CNN expects inputs of 224x224 resolution, and will classify objects into one of 1,000 possible categories.
These programs could take hours to run, and your computer's CPU will be maxed out during that time! Don't run HandWritingRecc2.ipynb unless you can tie up your computer for a long time. It will print progress as each epoch is run, but each epoch can take around 20 minutes.