Welcome back. So, in this notebook, you’ll be building your own neural network to classify clothing images. So, like I talked about in the last video, MNIST is actually a fairly trivial dataset these days. It’s really easy to get really high accuracy with a neural network. So instead, you’re going to be using Fashion-MNIST, and this is basically just a drop-in replacement for MNIST so we have 28 by 28 grayscale images, but this time it’s clothing. So, you have a lot more variation in the classes, and it just ends up being a much more difficult problem to classify like there’s a t-shirt, there’s pants, there’s a sweater, there’s shoes instead of handwritten digits. So it’s a better representation of datasets that you’d use in the real world. So, I’ve left this up to you to actually build a network and train it. So here you can define your network architecture, then here you will create your network to define the criterion and optimizer and then write the code for the training pass. Once you have your network built and trained, you can test out your network. So here, you’d want to do a forward pass, get your logits, calculate the class probabilities, maybe output of your network, and then pass in one of these images from the test set and check out if your network can actually predict it correctly. If you want to see my solution, it’s in the next notebook, part five, and you’ll also see it in the next video. Cheers.