Summary

In this chapter, we refreshed deep learning concepts such as layers, activation functions, and optimization schedules and how they contribute towards building varied deep learning architectures. We explored the PyTorch deep learning library, including some of the important modules, such as torch.nn, torch.optim, and torch.data, as well as tensor modules.

We then ran a hands-on exercise on training a deep learning model from scratch. We built a CNN for our exercise using PyTorch modules. We also wrote relevant PyTorch code to load the dataset, train and evaluate the model, and finally, make predictions from the trained model.

In the next chapter, we will explore a slightly more complex model architecture that involves multiple sub-models and use this type of hybrid model to tackle the real-world task of describing an image using natural text. Using PyTorch, we will implement such a system and generate captions for unseen images.