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PragatiVerma18
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Hey @ericgeiger1, Thanks for contributing to Codecademy docs. I have some suggestions; please go through them and make the required changes. Let me know if you need any help or have any questions.
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| ## Custom Loss Functions | ||
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| Using functional APIs, you can easily define custom loss functions. This is useful when the predefined loss functions in PyTorch do not meet your requirements. |
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| Using functional APIs, you can easily define custom loss functions. This is useful when the predefined loss functions in PyTorch do not meet your requirements. | |
| Using functional API, you can easily define custom loss functions. This is useful when the predefined loss functions in PyTorch do not meet your requirements. |
| - Cross-Entropy Loss | ||
| - Binary Cross-Entropy Loss | ||
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| # Apply linear transformation | ||
| output = F.linear(x, weight, bias) | ||
| ``` | ||
| The code will output a tensor of shape `(10, 5)` representing the result of the linear transformation. However, the actual values of the tensor will be random due to the use of `torch.randn`, which generates random values from a normal distribution. |
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| ### Using Linear Transformation | ||
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| ```python |
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| ```python | |
| ```py |
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| > **Note**: Each run generates random values for `x`, so the exact output will vary. | ||
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| ## Advantages of Using Functional APIs |
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| ## Advantages of Using Functional APIs | |
| ## Advantages of Using Functional API |
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Hey @ericgeiger1, Thanks for contributing to Codecademy docs. I have some suggestions; please go through them and make the required changes. |
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Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
Co-authored-by: Radhika-okhade <radhika.9628@gmail.com>
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Closes #5864
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mainbranch.Issues Solvedsection.