By Vivek Pandey
Implement a Convolutional Neural Network model to classify MNIST handwritten digit images using Keras and Python
CNN is basically a model known to be Convolutional Neural Network and in recent times it has gained a lot of popularity because of its usefulness. CNN uses multilayer perceptrons to do computational works. CNN uses relatively little pre-processing compared to other image classification algorithms. This means the network learns through filters that in traditional algorithms were hand-engineered. So, for the image processing task CNNs are the best-suited option
The MNIST database of handwritten digits has a training set of 60,000 examples and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to learn about various pattern recognition methods for real-world data while spending minimal effort on preprocessing and formatting.
We can get 99.09% accuracy by using CNN(Convolutional Neural Network) model.
Submitted by Vivek Pandey (vivekpandey496)
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