Image Classification Tutorial-1.2(MNIST Hand Written digit datasets using Neural Networks)
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In this exciting tutorial we are going to train our first Neural Network, this video is continuation of my last video in which we learnt how to load the MNIST hand written digit datasets and with using Matplotlib how to plot it. If you haven't watched it is recommended to watch that first, so here is the link: • Image Classification Tutorial-1.1(MNIST Ha... .
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 Neural Networks. You will also learn the most importance concept of Normalization before training the neural networks, I have stretched normalization a bit because this is also important when we will start the solving Kaggle datasets using Pandas. Moreover, you will understand the concept of "to_categorical" for one hot encoding of image labels followed by two most important activation functions namely "Relu" and "Softmax". In addition, you will learn the very famous Dense layer for constructing the hidden layers and before that you will learn what is "Flatten layer" and importance of Flatten layer. Then you will learn the importance concept of Validation Split, why we have to split or complete datasets in three parts Train,Test and Validation datasets.
Once you have understand all the basic concepts we are ready to train our Neural Network to recognize the MNIST hand written digit datasets.After training neural network we will check the training and validation accuracy and loss by plotting the data. Also, we have to check that how neural network 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 learn the concept of Convolutional Neural Networks and again we will take the same MNIST hand written datasets and we will train CNN to classify hand written digits.
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