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# Hands-on | PyTorch Assignment Help ## PyTorch Tensors:

• Create a PyTorch tensor with values from 0 to 9.

• Create a PyTorch tensor of size (3, 3) with all elements initialized to 1.

• Create a PyTorch tensor with random values of size (2, 3, 4).

• Create a PyTorch tensor of size (5, 5) with all elements initialized to 0.

• Add two PyTorch tensors of the same size.

• Multiply two PyTorch tensors element-wise.

## PyTorch Operations:

• Create a PyTorch tensor and compute the sum of its elements.

• Create a PyTorch tensor and compute the mean of its elements.

• Create a PyTorch tensor and compute its standard deviation.

• Create a PyTorch tensor and compute the element-wise absolute values.

• Create a PyTorch tensor and compute the natural logarithm of its elements.

• Create a PyTorch tensor and compute the exponential of its elements.

• Create a PyTorch tensor and set its requires_grad attribute to True.

• Create a PyTorch tensor and define a computation that involves it and some other tensor.

• Compute the gradient of the computation with respect to the tensor with requires_grad=True.

• Compute the gradient of a more complex computation with respect to multiple tensors.

• Use the detach() method to stop tracking the computation history of a tensor.

## PyTorch nn.Module:

• Define a simple feedforward neural network with one hidden layer.

• Define a loss function (e.g. mean squared error) and an optimizer (e.g. stochastic gradient descent).

• Train the neural network on a small dataset (e.g. the iris dataset).

• Evaluate the trained neural network on a test set and compute its accuracy.

• Save and load the trained neural network to/from disk.

• Write a custom dataset class that loads the dataset and preprocesses it.