Guide to freezing layers in AI models

Freezing layers in AI models is a transfer learning technique that prevents specific neural network weights from updating during training. This allows companies to build on pre-trained models by retaining useful learned features while adapting only necessary components to new tasks. By strategically freezing early layers (which capture universal patterns) and training only later layers, organizations can reduce computing costs, accelerate training, and achieve better results with limited datasets. The technique requires understanding which layers to freeze based on their function and is straightforward to implement in frameworks like Keras.