Neural Network For Handwritten Digits Classification | Deep Learning Tutorial 7 (Tensorflow2.0)

In this video we will build our first neural network in tensorflow and python for handwritten digits classification. We will first build a very simple neural network with only input and output layer. After that we will add a hidden layer and check how the performance of our model changes.
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Do you want to learn technology from me? Check codebasics.io/ for my affordable video courses

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Hello, why do we have 1875 steps in each epoch in 20:40 ? Where is this 1875 number coming from? It is not the 60000 training data size or nothing else we saw above that line? Thank you so much by the way..,

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Phani, exactly. I am following exactly same principal. There are many things in this tutorial such as loss, optimizer etc which remains mystery but I wanted to use that first and than unveil the mystery step by step :)

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from the Optimizers ['SGD','RMSprop','Adam','Adadelta','Adagrad','Adamax','Nadam','Ftrl'] with 5 Ephocs, Adamax got the highest score on Test samples. Ftrl got the lowest score.

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I am happy this was helpful to you.

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Very well planned and simply explained tutorial. But I have a question, I have read that for multi-class classification problem, softmax activation is good, why did you use sigmoid activation here? Thanks.

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