How does Model Parallelism distribute the computational workload of a neural network?a.By duplicating the training datab.By combining multiple models into a single devicec.By running multiple models on the same datasetd.By splitting a single neural network across multiple devices

Model Parallelism distributes the computational workload of a neural network by splitting a single neural network across multiple devices. Here's a step-by-step explanation:

1. Identify the Neural Network: The first step is to identify the neural network that needs to be trained. This network is usually a large one, which would take a lot of time and resources if trained on a single device.

2. Split the Network: The next step is to split the neural network into smaller parts. Each part should be able to run independently of the others. This is the key step in model parallelism.

3. Distribute the Parts: Each part of the neural network is then assigned to a different device. These devices can be different CPUs, GPUs, or even different machines.

4. Train the Network: Each device then trains its part of the network independently. This is done simultaneously, which is why model parallelism can speed up the training process.

5. Combine the Results: Once all devices have finished training their part of the network, the results are combined. This gives the final, trained neural network.

So, the correct answer to your question is d. By splitting a single neural network across multiple devices.