👉 Forward computing, also known as the forward pass in neural networks, is a crucial step in the training process where input data flows through the network sequentially, layer by layer, to produce an output. During this pass, each neuron in a layer computes a weighted sum of its inputs, applies an activation function to this sum, and passes the result to the next layer. This process continues until the final layer produces the network's prediction, such as a classification or regression output. Forward computing is essential for training neural networks as it allows the network to learn and adjust its weights based on the difference between its predicted output and the actual target values, typically measured using a loss function. This step is foundational for subsequent backward passes, where gradients are calculated and used to update the network's parameters.
