Summary

In this chapter, we learned about the building blocks of PyTorch – tensor objects – and performing various operations on top of them. We proceeded further by building a neural network on a toy dataset, where we started by building a class that initializes the feed-forward architecture, fetching data points from the dataset by specifying the batch size, and defining the loss function and the optimizer, looping through multiple epochs. Finally, we also learned about defining custom loss functions to optimize a metric of choice and leveraging the sequential method to simplify the process of defining the network architecture. All these steps form the foundation of building a neural network, which will be leveraged multiple times in the various use cases that we will build in subsequent chapters.

With this knowledge of the various components of building a neural network using PyTorch, we will proceed to the next chapter, where we will learn about the various practical...