Base Network in Neural Networks (AI/Deep Learning)
In the context of AI and deep learning, a base network refers to the underlying model or architecture that serves as the foundation for more advanced networks. This base network is typically pre-trained on large datasets and can be fine-tuned for specific tasks. A few examples:

Convolutional Neural Networks (CNNs): For image-related tasks, a CNN like ResNet or VGG might be used as the base network. These networks are designed to recognize patterns and features in images, and their weights can be fine-tuned to specific problems (e.g., facial recognition, object detection).

Transfer Learning: A pre-trained model like GPT (for text generation) or BERT (for language tasks) serves as a base network. The model has been trained on massive datasets and can be adapted to specific tasks (like sentiment analysis, translation, etc.) by fine-tuning.