Highlights: Hello everyone and welcome back. In the last posts we have seen some basic operations on what tensors are, and how to build a Shallow Neural Network. In this post we will demonstrate how to build efficient Convolutional Neural Networks using the nn module In Pytorch. You will find that it is simpler and more powerful. To demonstrate how it works, we will be using a dataset called MNIST. Tutorial Overview: Setting up the…
Read more

Highlights: Welcome everyone! In this post we are going to cover the prerequisites for installing PyTorch on our machines. In addition, we will show how you can use PyTorch in Google Colab. It’s relatively easy to get it up and running, so let’s roll. Tutorial Overview: PyTorch installation with Anaconda How to verify the installation? Using Google Colab 1. PyTorch installation with Anaconda Due to simplicity, the recommended option to start with is to use…
Read more

Highlights: Hello everyone and welcome back. In the previous post we have seen how to build one Shallow Neural Network and tested it on a dataset of random points. In this post we will demonstrate how to build efficient Neural Networks using the nn module. That means that we are going to use a fully-connected ReLU network with one hidden layer, trained to predict the output \(y \) from given \(x \) by minimizing squared…
Read more

Highlights: Welcome everyone! In this post we will learn how to use PyTorch for building a shallow neural network. If you want to go through a much detailed theoretical explanation about neural networks, and in particular a shallow neural network you can check out these blog posts. So, let’s start implementing one in PyTorch. Tutorial Overview: Data Preparation Define Model Structure Loss Function (Criterion) and Optimizer Model Training Testing our Model Visualize our Predictions 1.…
Read more