Ever thought about building you own neural network from scratch by simply using NumPy? In this code example, we will do exactly that. We will build a simple feedforward neural network and train it on the MNIST dataset. The MNIST dataset is a collection of 28x28 pixel grayscale images of handwritten digits (0-9). It is a popular dataset for getting started with machine learning and computer vision. The dataset contains 60,000 training images and 10,000 test images. The goal is to train a model that can correctly classify the images into their respective digit classes.

The entire tutorial can be found in this blog post.

For reproducibility:

conda create -n numpy_ann python=3.11
conda activate numpy_ann
conda install -y mamba
mamba install -y numpy matplotlib keras ipykernel

If you want to run the code on an Apple Silicon chip, follow these instructions to install TensorFlow (required by Keras).