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Deep Learning with Pytorch-DataLoader,Validation&Test,Dropouts – 1.2

Posted on January 27, 2019January 27, 2019 by Aritra Sen

In this previous post , we saw how to train a Neaural Network in Pytorch with different available modules.
In this tutorial we will go through different functionalities of Pytorch like Data Loader ,Subsetsampler and how to create Validation and Test Set with the help of Data Loader.
One of the main problem of Neaural Network is over-fitting which we can be tackled with Dropout. Dropout implementation we will also go through in this tutorial. We will use the MNIST dataset to see how these concepts are implemented through Pytorch.

Neaural Network with Dropout


Let’s get out hands dirty by doing all these in Pytorch.

Category: Machine Learning, Python

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← Deep Learning with Pytorch – Neural Network Implementation – 1.1
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