Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Automatic Differentiation PyTorch

The video tutorial introduces automatic differentiation in PyTorch, focusing on defining and optimizing loss functions. It explains manual gradient computation and demonstrates how PyTorch automates this process using tensors and the backward method. The tutorial covers setting up parameters for gradient computation and highlights the simplicity of using PyTorch for complex neural network architectures. The video concludes with a brief overview of building neural networks using automatic differentiation.

What are the values of A and B used in the example, and how do they affect the gradient?

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Describe the process of defining a loss function in Pytorch as mentioned in the text.

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What is the role of automatic differentiation in training neural networks?

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