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bookChallenge: Implementing Linear Regression

Task

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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.

  1. Convert these columns into PyTorch tensors, and reshape them to ensure they are 2D with shapes [N, 1].
  2. Define a simple linear regression model.
  3. Use MSE as the loss function.
  4. Define optimizer as SGD with the learning rate equal to 0.01.
  5. 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.
  6. Access the model's parameters (weights and bias).

Solution

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SectionΒ 2. ChapterΒ 4
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bookChallenge: Implementing Linear Regression

Task

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.

  1. Convert these columns into PyTorch tensors, and reshape them to ensure they are 2D with shapes [N, 1].
  2. Define a simple linear regression model.
  3. Use MSE as the loss function.
  4. Define optimizer as SGD with the learning rate equal to 0.01.
  5. 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.
  6. Access the model's parameters (weights and bias).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 4
single

single

close

Awesome!

Completion rate improved to 5

bookChallenge: Implementing Linear Regression

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Task

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.

  1. Convert these columns into PyTorch tensors, and reshape them to ensure they are 2D with shapes [N, 1].
  2. Define a simple linear regression model.
  3. Use MSE as the loss function.
  4. Define optimizer as SGD with the learning rate equal to 0.01.
  5. 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.
  6. Access the model's parameters (weights and bias).

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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