Training

Training is the process where a machine learning model learns patterns from data by adjusting its parameters to minimize prediction errors.

Training Process: 1. Forward Pass: Data flows through the model 2. Loss Calculation: Measure error between prediction and truth 3. Backward Pass: Calculate how to adjust parameters 4. Update: Modify parameters to reduce error 5. Repeat: Iterate over many examples (epochs)

• **Key Concepts:**
• Epochs: Complete passes through the training data
• Batch Size: Number of examples processed together
• Learning Rate: How much to adjust parameters each step
• Loss Function: Measures prediction error