The training process begins with feeding input data into a selected machine learning model. The model then learns from this data by adjusting its internal parameters to minimize errors. The training workflow consists of the following steps:
Forward Propagation – The input passes through the model, and an initial prediction is generated.
Loss Calculation – The model measures how far its prediction is from the actual outcome using a loss function.
Backpropagation – The model calculates gradients and updates parameters to reduce errors.
Optimization – Techniques such as gradient descent help the model converge to an optimal state.
Validation – The model is tested on unseen data to ensure its ability to generalize.
Training can take minutes, hours, or even days depending on the dataset size and complexity of the model. Deep learning models, in particular, require extensive computational resources to process large volumes of data efficiently.
