Convolutional Neural Networks with Pytorch - Free Download Tutorials

In a previous introductory tutorial on neural networks, a three layer neural network was developed to classify the hand-written digits of the MNIST dataset. In the end, it was able to achieve a classification accuracy around 86%. For a simple data set such as MNIST, this is actually quite poor. Further optimizations can bring densely connected networks of a modest size up to 97-98% accuracy. This is significantly better, but still not that great for MNIST. We need something more state-of-the-art, some method which can truly be called deep learning. This tutorial will present just such a deep learning method that can achieve very high accuracy in image classification tasks – the Convolutional Neural Network. In particular, this tutorial will show you both the theory and practical application of Convolutional Neural Networks in PyTorch.

PyTorch is a powerful deep learning framework which is rising in popularity, and it is thoroughly at home in Python which makes rapid prototyping very easy. This tutorial won’t assume much in regards to prior knowledge of PyTorch, but it might be helpful to checkout my previous introductory tutorial to PyTorch. All the code for this Convolutional Neural Networks tutorial can be found on this site’s Github repository – found here. Let’s get to it.

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