You've learned about the forward pass, calculating loss, and the backward pass with backpropagation. Let's put these pieces together and practice identifying the steps of the complete training loop.
Engagement Message
Ready to see the full picture?
Type
Fill In The Blanks
Markdown With Blanks
Fill in the blanks to put the main steps of a training loop in order.
The process begins with a [[blank:forward pass]] to get a prediction. Then, we use a [[blank:loss function]] to measure the error. After that, [[blank:backpropagation]] calculates the gradients. Finally, we [[blank:update]] the weights to improve the model.
Suggested Answers
- forward pass
- loss function
- backpropagation
- update
Type
Swipe Left or Right
Practice Question
Swipe to categorize these actions into the correct phase of training.
Labels
- Left Label: Forward Pass
- Right Label: Backward Pass
Left Label Items
- Making a prediction
- Applying activation functions
- Data flows from input to output
Right Label Items
- Calculating gradients
- Propagating error signal
- Updating weights
Type
Multiple Choice
Practice Question
What is the direct input to the backpropagation algorithm?
A. The initial data B. The network's prediction C. The calculated loss D. The learning rate
Suggested Answers
- A
- B
- C - Correct
- D
Type
Sort Into Boxes
Practice Question
Sort these concepts based on what they accomplish.
Labels
- First Box Label: Measures Error
- Second Box Label: Reduces Error
First Box Items
- Loss Function
- MSE
Second Box Items
- Gradient
- Backpropagation
- Weight Update
Type
Multiple Choice
Practice Question
If a network's loss is very high, what does this imply about the gradients calculated during backpropagation?
A. The gradients will likely be large, leading to a significant weight update. B. The gradients will be zero, so no learning can happen. C. The gradients will be negative, causing the network to get worse. D. The gradients are unrelated to the loss value.
Suggested Answers
- A - Correct
- B
- C
- D
