Image Classifiaction Tutorial-2 (MNIST hand written datasets using Convolutional Neural Networks)
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In today's exciting tutorial we are going to train Convolutional Neural Network to classify MNIST hand written digit datasets.
Let me give you quick snapshot of today's tutorial so, In this video we are going to learn the architecture and brief concept of Convolutional Neural Networks. You will also learn the most importance concept of Convolution before training the CNN. In addition, you will learn the very famous Dense layer for constructing the hidden layers and before that you will learn what is "Convolutional layer" and "Max Polling".
Once you have understand all the basic concepts we are ready to train our Convolutional Neural Network to recognize the MNIST hand written digit datasets. After training CNN we will check the training and validation accuracy and loss by plotting the data. Also, we have to check that how CNN will perform on the unseen test datasets.
So this tutorial is complete package for those who want to learn deep learning and neural network. In my next tutorial we will take Fashion MNIST datasets and we will train NN and CNN and we will see the how it will perform on unseen images from test datasets. So far we have just seen the accuracy and loss but we never checked on images. So in my next tutorial we will test on images as well and also we will see the very important concept of "Confusion Matrix" and "Classification Report" to check the model accuracy.
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