The algorithm you're referring to is called Stochastic Gradient Descent with Momentum (SGD with Momentum). Here's a step-by-step explanation:

1. Initialize the weights (parameters) randomly.

2. Calculate the gradient of the loss function with respect to each parameter at the current position.

3. Instead of updating the parameters immediately, the gradient is used to update a 'velocity' vector.

4. This velocity vector is then used to update the parameters. The velocity vector is multiplied by a factor (momentum) between 0 and 1 before being added to the parameters.

5. The momentum term increases for dimensions whose gradients point in the same directions and reduces updates for dimensions whose gradients change directions. This means the parameter updates take into consideration the past gradients to dampen the oscillations.

6. Repeat steps 2-5 until the algorithm converges to the minimum.

The addition of momentum helps the algorithm to navigate along the relevant directions and softens the oscillations in the irrelevant. It's like a ball rolling downhill, it will tend to go in the same direction and won't oscillate in orthogonal directions. This leads to faster convergence and reduced training time.

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The algorithm you're referring to is called Stochastic Gradient Descent with Momentum (SGD with Momentum). Here's a step-by-step explanation:

1. Initialize the weights (parameters) randomly.

2. Calculate the gradient of the loss function with respect to each parameter at the current position.

3. Instead of updating the parameters immediately, the gradient is used to update a 'velocity' vector.

4. This velocity vector is then used to update the parameters. The velocity vector is multiplied by a factor (momentum) between 0 and 1 before being added to the parameters.

5. The momentum term increases for dimensions whose gradients point in the same directions and reduces updates for dimensions whose gradients change directions. This means the parameter updates take into consideration the past gradients to dampen the oscillations.

6. Repeat steps 2-5 until the algorithm converges to the minimum.

The addition of momentum helps the algorithm to navigate along the relevant directions and softens the oscillations in the irrelevant. It's like a ball rolling downhill, it will tend to go in the same direction and won't oscillate in orthogonal directions. This leads to faster convergence and reduced training time.

Knowee AI · Accepted Answer

The algorithm you're referring to is called Stochastic Gradient Descent with Momentum (SGD with Momentum). Here's a step-by-step explanation:

1. Initialize the weights (parameters) randomly.

2. Calculate the gradient of the loss function with respect to each parameter at the current position.

3. Instead of updating the parameters immediately, the gradient is used to update a 'velocity' vector.

4. This velocity vector is then used to update the parameters. The velocity vector is multiplied by a factor (momentum) between 0 and 1 before being added to the parameters.

5. The momentum term increases for dimensions whose gradients point in the same directions and reduces updates for dimensions whose gradients change directions. This means the parameter updates take into consideration the past gradients to dampen the oscillations.

6. Repeat steps 2-5 until the algorithm converges to the minimum.

The addition of momentum helps the algorithm to navigate along the relevant directions and softens the oscillations in the irrelevant. It's like a ball rolling downhill, it will tend to go in the same direction and won't oscillate in orthogonal directions. This leads to faster convergence and reduced training time.

is an optimization algorithm that combines the benefits of gradient descent and momentum to accelerate convergence

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