In this third and final part of the Introduction to pyTorch series, we’ll be building neural networks.

In the second part of this three-part introduction to pyTorch, I’ll be looking at automatic differentiation and tesnor operations.

In this 3-part series, I’ll be going through some basic tensor operations in pyTorch.

Here is an overview of some common mathematical formulas from the field of neural networks.

A brief intro into recurrent neural networks.

A brief intro into deep learning & convolutional neural networks.

All about RBMs.

In this post we look at Hebbian Learning, Gibbs Sampling and Hopfield Networks.

Although not strictly a neural net, here we introduce the Support Vector Machine.

In this post, I take a look at self-organising maps.

In this tutorial, we dive into backprop and build our first simple neural network.

In this notebook, I look back through the history of neural networks.