AI / ML

Pre-trained models like BERT or ResNet provide a great starting point, but fine-tuning can unlock their true potential for specific tasks. By updating the weights on task-specific data while retaining the base model's architecture, you can achieve remarkable results. Key tip: use learning rate schedulers and layer freezing strategically to avoid overfitting and speed up training. Fine-tuning bridges the gap between general-purpose AI and tailored solutions.