Podcast
Questions and Answers
- Autograd calculates and stores the gradients for each model parameter in the parameter's attribute during ______ propagation.
- Autograd calculates and stores the gradients for each model parameter in the parameter's attribute during ______ propagation.
backward
- We register all the parameters of the model in the ______, which adjusts each parameter by its gradient stored in autograd's attribute.
- We register all the parameters of the model in the ______, which adjusts each parameter by its gradient stored in autograd's attribute.
optimizer
- Autograd tracks operations on all tensors that have their flag set to True for ______ computation.
- Autograd tracks operations on all tensors that have their flag set to True for ______ computation.
gradient
- The output tensor of an operation will require gradients even if only a single input tensor has ______=True.
- The output tensor of an operation will require gradients even if only a single input tensor has ______=True.
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- In finetuning, we freeze most of the model and typically only modify the ______ layers to make predictions on new labels.
- In finetuning, we freeze most of the model and typically only modify the ______ layers to make predictions on new labels.
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- Autograd keeps a record of data and all executed operations in a directed acyclic graph (DAG) consisting of ______ objects.
- Autograd keeps a record of data and all executed operations in a directed acyclic graph (DAG) consisting of ______ objects.
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- Exclusionary functionality is available in ______() for tensors that don't require gradients.
- Exclusionary functionality is available in ______() for tensors that don't require gradients.
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- The two steps of training a neural network are forward propagation and ______ propagation.
- The two steps of training a neural network are forward propagation and ______ propagation.
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Study Notes
Introduction to PyTorch's Automatic Differentiation Engine - torch.autograd
- PyTorch's automatic differentiation engine, torch.autograd, powers neural network training.
- Neural networks consist of nested functions executed on input data, defined by parameters stored in tensors.
- The two steps of training a neural network are forward propagation and backward propagation.
- Forward propagation makes a guess about the correct output and collects the derivatives of the error with respect to the parameters of the functions (gradients) in backward propagation.
- Autograd calculates and stores the gradients for each model parameter in the parameter's attribute during backward propagation.
- We register all the parameters of the model in the optimizer, which adjusts each parameter by its gradient stored in autograd's attribute.
- Autograd tracks operations on all tensors that have their flag set to True for gradient computation.
- The output tensor of an operation will require gradients even if only a single input tensor has requires_grad=True.
- In finetuning, we freeze most of the model and typically only modify the classifier layers to make predictions on new labels.
- Autograd keeps a record of data and all executed operations in a directed acyclic graph (DAG) consisting of Function objects.
- The graph is recreated from scratch after each call, allowing control flow statements in the model.
- Exclusionary functionality is available in torch.no_grad() for tensors that don't require gradients.
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Description
Test your knowledge of PyTorch's automatic differentiation engine with this quiz! Learn about the forward and backward propagation steps involved in training a neural network, how autograd calculates and stores gradients, and the functionality of the directed acyclic graph (DAG). This quiz is perfect for those who want to deepen their understanding of PyTorch's powerful tool for deep learning.
