Solution

The correct answer is (A), (B), (D), (C), (E).

Key Points

• Weight Initialization: This is the first step in training a neural network. It involves setting the initial weights for the network, which can be done randomly or using some heuristic.
• Feed Forward: Once the weights are initialized, the input data is passed through the network to generate an output. This process is called feed forward.
• Loss Calculation: After the feed forward step, the loss (or error) is calculated by comparing the network's output with the actual target values. This loss quantifies how well the network is performing.
• Back Propagation: In this step, the error calculated in the previous step is propagated back through the network to update the weights. This is done using the gradient descent algorithm to minimize the loss.
• Weight Update: Finally, the weights are updated based on the gradients computed during back propagation. This completes one iteration of training.

Thus the correct answer is ​(A), (B), (D), (C), (E).

Additional Information

•  The process of training a neural network is iterative and involves multiple passes through these steps. Each pass is known as an epoch. With each epoch, the network's performance typically improves as the weights are fine-tuned.
• Proper weight initialization is crucial as poor initialization can lead to slow convergence or getting stuck in local minima.
• The choice of loss function and optimization algorithm can significantly impact the training process and the final performance of the neural network.