PyTorch Autograd: Automatic Differentiation Explained
Last Updated on November 25, 2025 by Editorial Team
Author(s): Alok Choudhary
Originally published on Towards AI.
PyTorch Autograd: Automatic Differentiation Explained
PyTorch Autograd is the backbone of PyTorch’s deep learning ecosystem, providing automatic differentiation for all tensor operations. This feature eliminates the need for manually deriving gradients, which is essential when training models with optimization algorithms such as Gradient Descent.
The article explains the function and significance of PyTorch’s Autograd system, detailing how it automates the process of automatic differentiation in deep learning. It elaborates on the essential components and processes involved in training neural networks, including the forward and backward passes, loss calculations, and parameter updates. Key concepts such as computation graphs and gradient tracking are also introduced, highlighting how these tools alleviate the cumbersome task of manual differentiation and improve efficiency in model training.
Read the full blog for free on Medium.
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