Building Your First Network in PyTorch

A summary to kickstart your deep learning career.

Tim Cheng
Towards Data Science

Photo by Max Duzij on Unsplash

Starting a deep learning project sounds scary and difficult? I have read through articles, took lessons, and watched videos about neural networks, but how do I begin programming one? We have all been through that stage, and this is why I am creating this article to tell you everything (or at least most of the things I know) to begin your PyTorch model training project.

The guide is presented in a bottom-up way. I will first describe individual components that are important to training a deep network, then provide examples on how to combine all the components together for training and testing.

Side Note:

The article serves as a bridging medium for converting the theoretical knowledge in ML directly into codes. Prior ML knowledge is assumed.

My projects are mainly in the domain of computer vision, and so what I found to be the most useful functions in PyTorch are also biased towards applications regarding images.

Table of Contents

· Table of Contents
· Import
· Network Components
Fully-Connected Layers
The Convolution Family
Recurrent Networks
Activation functions
· Loss Functions
· Optimisers
· Setting up GPU
· Combining

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