This chapter was divided into two sections. In the first section, we investigated the Quick Draw! dataset from Google. We introduced it and then we saw how to load it into memory. This was straightforward as Google has kindly made the dataset available as a set of .npy files, which can be loaded directly into NumPy arrays. Next, we divided the data into training, validation, and test sets. After creating our ConvNet model, we trained it on the data and tested it. In the tests, over 25 epochs, the model achieved an accuracy of just over 90%, and we noted that this could probably be improved upon with further tweaking of the model. Lastly, we saw how to save a trained model and then how to reload it and use it for further inference.
In the second section, we trained a model to recognize images in the CIFAR 10 image dataset. This dataset consists of 10 classes of images and...
