To Develop a convolutional deep neural network for digit classification and to verify the response for scanned handwritten images.
The MNIST dataset is a collection of handwritten digits. The task is to classify a given image of a handwritten digit into one of 10 classes representing integer values from 0 to 9, inclusively. The dataset has a collection of 60,000 handwrittend digits of size 28 X 28. Here we build a convolutional neural network model that is able to classify to it's appropriate numerical value.
Download and load the dataset
Scale the dataset between it's min and max values
Using one hot encode, encode the categorical values
Split the data into train and test
Build the convolutional neural network model
Train the model with the training data
Plot the performance plot
Evaluate the model with the testing data
https://github.com/yoursenpai69/dl-exp3/blob/main/Copy_of_Ex03_minist_classification.ipynb
Successfully developed a convolutional deep neural network for digit classification and verified the response for scanned handwritten images.