Training Deep Neural Networks: The Learning Process
Training a deep neural network involves feeding it large amounts of labeled data and allowing it to adjust its parameters through multiple iterations. The process follows these key steps:
Forward Propagation – The input data moves through the network, and each neuron applies mathematical transformations to extract patterns. The final layer produces an output (prediction).
Loss Calculation – The model calculates how far its prediction is from the actual correct value using a loss function (such as Mean Squared Error for regression tasks or Cross-Entropy Loss for classification tasks).
Backpropagation – The network propagates the error backward, adjusting weights and biases to reduce errors in future iterations.
Optimization with Gradient Descent – The network updates its parameters using optimization algorithms like Stochastic Gradient Descent (SGD), Adam, or RMSprop to gradually improve accuracy.
Epochs and Iterations – The model repeats this process over multiple epochs (complete passes through the dataset) until it achieves optimal performance.
