Now that we've covered the basics of deep learning, it's time to get our taste buds involved. In this step, you'll learn how to use a neural network to taste-taste the difference between a taco and a tartine.
We'll be using a pre-trained model that's been trained on a dataset of delicious food images. Don't worry, it's a real dataset (sort of).
First, we need to prepare our data. This involves collecting images of tacos and tartines, and annotating them with labels like "taco" or "tartine".
We'll use a simple convolutional neural network to process the images and extract features. This will give us a vector representation of each image, which we can then use to train our model.
Our model will be trained using a loss function that takes into account the difference between the predicted label and the true label. We'll use a variant of the popular "categorical cross-entropy" loss function, but with a twist: we'll add a penalty for incorrect tacos.
Next Step: Step 7 - Model Deployment Previous Step: Step 4 - Data Preprocessing Back to Project Transfer Learning