<ul><li>The article discusses the problem of gradient instability in PyTorch and provides a solution to fix it.</li><li>The issue arises when the input features are out of scale, causing one feature to have a more dominant effect on the prediction than others.</li><li>One solution is to increase the number of iterations in the training loop, but it may take more time to converge to an optimum minima.</li><li>A recommended approach is to normalize the input features using min-max normalization, which scales the features within a comparable range and ensures smoother convergence.</li></ul>

A practical look at Gradient Instability and how to fix it using PyTorch

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