Convolutional Neural Network (CNN)
Handwritten Digit classification using MNIST dataset. MNIST is a dataset of 60,000 training set images of handwritten single digits between 0 and 9, each image is a 28×28 pixel square.
The task is to classify a given image of a handwritten digit into one of 10 classes representing integer
values from O to 9, inclusively.
Do Pre-processing step (Normalization). Rescale pixel values to the range [0-1].
- Convert Datatype of pixels to float
- Divide each image by 255.
Build a 4 different architecture convolutional neural network model that can detect the digit of a given image. (Change number of convolutional layer, pooling layers, Apply cross validation during training. The training dataset is shuffled prior to being split.
Evaluate your model’s using accuracy.
