👉 Slope computing, also known as gradient computation, is a fundamental process in machine learning and deep learning that quantifies the rate of change of a function, typically a loss or error function, with respect to its input parameters. In the context of neural networks, this involves calculating the derivative of the loss function with respect to each weight or parameter in the network. This derivative, known as the gradient, points in the direction of steepest ascent and its magnitude indicates how much the loss changes when the parameter is adjusted. By computing these gradients, optimization algorithms like gradient descent can update the parameters iteratively to minimize the loss, thereby improving the model's performance. Essentially, slope computing helps in understanding how sensitive the output of a function is to changes in its inputs, which is crucial for training models efficiently.
