Calculating Derivatives in PyTorch
Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for user-defined functions.
While we always have to deal with backpropagation (an algorithm known to be the backbone of a neural network) in neural networks, which optimizes the parameters to minimize the error in order to achieve higher classification accuracy; concepts learned in this article will be used in later posts on deep learning for image processing and other computer vision problems.
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