Reinforcement Learning and Deep RL Python Theory and Projects - Automatic Differentiation PyTorch

The video tutorial explains automatic differentiation in Pytorch, focusing on loss functions and their derivatives. It covers the calculation of partial derivatives for parameters A and B, using a specific function as an example. The tutorial demonstrates how to set up Pytorch for automatic differentiation, define loss functions, and compute gradients automatically. It highlights the benefits of using Pytorch for complex neural network architectures, eliminating the need for manual backpropagation and gradient computations.

What command is used to compute the gradient in Pytorch?

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What does setting 'requires_grad' to true do in Pytorch?

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Describe the process of automatic differentiation in Pytorch as mentioned in the text.

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