MNIST-Digit-Recognizer-CNN-Keras-99.66

MNIST DIGIT RECOGNIZER COMPETITION on Kaggle.

1. The Training Dataset has 42000 Images each of 784 pixels representing Digits from 0-9.
2. The Test Dataset has 28000 Images.
3. I have reshaped the Training and the Test Samples into 28 x 28 pixels for further processing.
4. I have used Convolutional Neural Networks to Train the Data.
5. After every layer, I have used Batch Normalization to transform the data points to have Zero Mean and Variance One.
6. I have used Dropout layers to avoid Overfitting.
7. I have performed Data Augmentation and generated random images with ImageDatagenerator to avoid Overfitting.
8. I have used LearningRateScheduler after every Epoch to Reduce the Learning Rate in order to converge to Local Optimum.
9. Then, I plot the Confusion Matrix to Evaluate the Performance of the model on the data.
10. Lastly, I have generated csv file and made submission on Kaggle and acheived 99.66% Accuracy on its Public Leaderboard.