Question at position 32 What are the steps for using a gradient descent algorithm in neural networks? Calculate error between the actual value and the predicted value Reiterate until you find the best weights of the network Pass an input through the network and get values from the output layer Initialize random weight and bias Go to each neuron that contributes to the error and change its respective values to reduce the error 5, 4, 3, 2, 14, 3, 1, 5, 23, 2, 1, 5, 41, 2, 3, 4, 5单项选择题
A
5, 4, 3, 2, 1
B
4, 3, 1, 5, 2
C
3, 2, 1, 5, 4
D
1, 2, 3, 4, 5

Question

Question at position 32 What are the steps for using a gradient descent algorithm in neural networks? Calculate error between the actual value and the predicted value Reiterate until you find the best weights of the network Pass an input through the network and get values from the output layer Initialize random weight and bias Go to each neuron that contributes to the error and change its respective values to reduce the error 5, 4, 3, 2, 14, 3, 1, 5, 23, 2, 1, 5, 41, 2, 3, 4, 5单项选择题

A

5, 4, 3, 2, 1

B

4, 3, 1, 5, 2

C

3, 2, 1, 5, 4

D

1, 2, 3, 4, 5

BlackTom AI · Accepted Answer

Gradient descent training begins by initializing the model’s parameters (weights and biases) randomly so the network has a starting point for learning.

1) Relevant concepts
- Initialization: choose random weights/biases to break symmetry.
- Forward pass: compute network outputs from inputs using current weights.
- Loss computation: quantify error between predicted and true outputs (e.g., MSE).
- Backpropagation + gradient descent: compute gradients of loss w.r.t. weights and update weights to reduce loss: w := w - η * ∂L/∂w.
- Iteration until convergence: repeat the cycle until loss is acceptably low.

2) Step-by-step reasoning matching the correct ordering
1. Initialize random weight and bias — must come first so the network has parameters to use.
2. Pass an input through the network and get values from the output layer (forward pass).
3. Calculate error between the actual value and the predicted value (compute loss).
4. Go to each neuron that contributes to the error and change its respective values to reduce the error (backpropagate gradients and update weights).
5. Reiterate until you find the best weights of the network (repeat until convergence).

3) Example calculation (intermediate steps)
- Simple single-weight neuron: y_pred = w*x (bias omitted). Let x = 2, y = 4, initial w = 1.
- Forward: y_pred = 1*2 = 2.
- Error (MSE half form): L = 0.5*(y - y_pred)^2 = 0.5*(4-2)^2 = 2.
- Gradient: ∂L/∂w = (y_pred - y) * x = (2-4)*2 = -4.
- Update with learning rate η = 0.1: w_new = w - η * ∂L/∂w = 1 - 0.1*(-4) = 1.4.
- Repeat: forward with new w, recompute loss, continue until convergence.

4) Why the correct answer is correct
This order follows the logical algorithmic flow: initialize → forward → loss → backprop/update → repeat. Each step depends on the previous (you cannot compute outputs before initializing, cannot compute gradients before computing loss, etc.).

5) Why other options are incorrect (brief)
- Any sequence that computes error before a forward pass or updates weights before initialization is logically impossible.
- Orders that place “reiterate” before doing an update or that swap forward and initialization violate dependency of steps.

Conclusion: The correct ordered sequence is 1, 2, 3, 4, 5, which matches the provided answer.

类似问题

Which if the following answers are correct regarding "Gredient descent"?Select all the correct answers.

Why does the gradient descent algorithm not work for training a linear classifier?

Which parameter determines the size of the improvement step to take on each iteration of Gradient Descent?

Optimization algorithms like gradient descent are typically used in machine learning for which of the following (select all that apply):

The learning rate (α - alpha) as related to gradient descent is best described as:

Which of the following statements about gradient descent and learning rate is true?

Which statement is correct?

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