Training & Optimization
Training is how machine learning models learn from data. The process involves feeding data through the model, measuring errors, and adjusting parameters to improve performance.
Training Process
- Forward Pass: Input data flows through the model
- Loss Calculation: Compare predictions to ground truth
- Backward Pass: Calculate gradients via backpropagation
- Parameter Update: Adjust weights using optimization algorithm
- Repeat: Iterate over the dataset multiple times (epochs)
Key Concepts
- Batch Size: Number of examples processed together
- Epoch: One complete pass through the training dataset
- Learning Rate: Step size for parameter updates
- Convergence: When training loss stabilizes
Challenges
- Overfitting, underfitting, vanishing gradients, computational cost
Tags
fundamentals training machine-learning
Related Terms
Backpropagation
The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.
Epoch
One complete pass through the entire training dataset during the training process.
Gradient Descent
An optimization algorithm that iteratively adjusts parameters in the direction of steepest descent of the loss function.
Loss Function
A function measuring the difference between model predictions and true values, guiding the training process.