Challenge: Implementing Linear Regression
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You are provided with a dataset that contains information about the number of hours students studied and their corresponding test scores. Your task is to train a linear regression model on this data.
- Convert these columns into PyTorch tensors, and reshape them to ensure they are 2D with shapes
[N, 1]
. - Define a simple linear regression model.
- Use MSE as the loss function.
- Define
optimizer
as SGD with the learning rate equal to0.01
. - Train the linear regression model to predict test scores based on the number of hours studied. At each epoch:
- Compute predictions on
X_tensor
; - Compute the loss;
- Reset the gradient;
- Perform backward pass;
- Update the parameters.
- Compute predictions on
- Access the model's parameters (weights and bias).
Lösning
Var allt tydligt?
Tack för dina kommentarer!
Avsnitt 2. Kapitel 4
single
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Challenge: Implementing Linear Regression
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Uppgift
Swipe to start coding
You are provided with a dataset that contains information about the number of hours students studied and their corresponding test scores. Your task is to train a linear regression model on this data.
- Convert these columns into PyTorch tensors, and reshape them to ensure they are 2D with shapes
[N, 1]
. - Define a simple linear regression model.
- Use MSE as the loss function.
- Define
optimizer
as SGD with the learning rate equal to0.01
. - Train the linear regression model to predict test scores based on the number of hours studied. At each epoch:
- Compute predictions on
X_tensor
; - Compute the loss;
- Reset the gradient;
- Perform backward pass;
- Update the parameters.
- Compute predictions on
- Access the model's parameters (weights and bias).
Lösning
Var allt tydligt?
Tack för dina kommentarer!
Awesome!
Completion rate improved to 5Avsnitt 2. Kapitel 4
single