Top Highlights

• We can see that the model has learned to create feature detectors that look for corners, repeating lines, circles, and other simple patterns.
• passing is_cat, which returns x[0].isupper(), which evaluates to True if the first letter is uppercase
• The most important parameter to mention here is valid_pct=0.2. This tells fastai to hold out 20% of the data and not use it for training the model at all
• When using a pretrained model, vision_learner will remove the last layer, since that is always specifically customized to the original training task (i.e. ImageNet dataset classification), and replace it with one or more new layers with randomized weights, of an appropriate size for the dataset you are working with. This last part of the model is k...
• But why is the method called fine_tune, and not fit? fastai actually does have a method called fit, which does indeed fit a model (i.e. look at images in the training set multiple times, each time updating the parameters to make the predictions closer and closer to the target labels). But in this case, we’ve started with a pretrained model, and we ...

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