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Keras Fully Connected Neural Network using Python for Digit Recognition

About Welcome to another tutorial on Keras. This tutorial will be exploring how to build a Fully Connected Neural Network model for Object Classification on Mnist Dataset. Let's get straight into it!

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 try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.

Note: For learners who are unaware how Convolutional Neural Newtworks work, here are some excellent links on 
the theoretical aspect:
- https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
- https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
- https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/