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Troubleshooting Guide: Windows 11 Taskbar Not Showing - How to Fix It

  If your Windows 11 taskbar is not showing, you can try several troubleshooting steps to resolve the issue. Here are some potential solutions you can try:
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Understanding the Differences between Batch Gradient Descent and Stochastic Gradient Descent

 Batch gradient descent and stochastic gradient descent are two popular optimization algorithms used in machine learning to train models. Here are the key differences between the two:

Batch Gradient Descent:

  1. Batch gradient descent updates the model's parameters after computing the gradient of the loss function over the entire training dataset.

  2. It is computationally expensive, as it requires storing the entire dataset in memory.

  3. It converges to the optimal solution, but may take a longer time to converge.

  4. It is less susceptible to the noise in the data, as it averages out the gradients over the entire dataset.

Stochastic Gradient Descent:

  1. Stochastic gradient descent updates the model's parameters after computing the gradient of the loss function for each individual training example.

  2. It is computationally efficient, as it only requires one example to be stored in memory at a time.

  3. It may converge to a suboptimal solution due to the high variance of the gradients, which can lead to noisy updates.

  4. It is more susceptible to the noise in the data, as it updates the parameters for each example individually.

  5. It can converge faster than batch gradient descent, but may require more iterations to reach the optimal solution.

Mini-Batch Gradient Descent:

Mini-batch gradient descent is a compromise between batch gradient descent and stochastic gradient descent. It updates the model's parameters after computing the gradient of the loss function over a small random subset of the training dataset.

In summary, the main difference between batch gradient descent and stochastic gradient descent is the size of the dataset used to compute the gradient of the loss function. Batch gradient descent uses the entire dataset, while stochastic gradient descent uses one example at a time. The choice of which algorithm to use depends on the size of the dataset, the available computational resources, and the desired convergence speed and accuracy.


Tags: Batch gradient descent vs stochastic gradient descent, Optimization algorithms in machine learning, Convergence speed in machine learning, Variance of gradients in machine learning, Computationally efficient machine learning algorithms, Gradient descent in machine learning, Mini-batch gradient descent in machine learning, Machine learning training algorithms

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