Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: Automatic Differentiation

The video tutorial explains automatic differentiation, focusing on PyTorch. It begins with an introduction to loss functions and the need to compute derivatives for optimization in machine learning. The tutorial then demonstrates how to calculate gradients manually and automatically using PyTorch. It provides a practical example of setting up parameters as tensors, defining a loss function, and using PyTorch's backward method to compute gradients automatically, highlighting the ease and efficiency of this approach.

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