Overview

This self-paced tutorial aims to provide a comprehensive, one-stop resource for understanding the fundamentals of neural networks without the need for GPUs or internet access. This tutorial series is split into five parts:

1. an overview of neural networks
2. the math behind forward and back propagation
3. MNIST classification with a MLP in NumPy
4. MNIST classification with a MLP in PyTorch
5. MNIST classification with a CNN in PyTorch Along the way, I provide offline PDF resources with more detailed and/or supplemental information.

Getting started

After completing the following steps, you will be able to get working with no need for anymore internet access or GPUs.