Summary
In this chapter, we covered PyTorch’s most essential and useful features. We started by discussing PyTorch’s dynamic computation graph, which makes implementing computations very convenient. We also covered the semantics of defining PyTorch tensor objects as model parameters.
After we considered the concept of computing partial derivatives and gradients of arbitrary functions, we covered the torch.nn module in more detail. It provides us with a user-friendly interface for building more complex deep NN models. Finally, we concluded this chapter by solving a regression and classification problem using what we have discussed so far.
Now that we have covered the core mechanics of PyTorch, the next chapter will introduce the concept behind convolutional neural network (CNN) architectures for deep learning. CNNs are powerful models and have shown great performance in the field of computer vision.
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