Summary
Our exploration of pre-trained models gave us insight into how these models, trained on extensive data and time, provide a solid foundation for us to build upon. They help us overcome constraints related to computational resources and data availability. Notably, we familiarized ourselves with image-based models such as VGG16 and ResNet, and text-based models such as BERT and GPT, adding them to our repertoire.
Our voyage continued into the domain of TL, where we learned its fundamentals, recognized its versatile applications, and acknowledged its different forms—inductive, transductive, and unsupervised. Each type, with its unique characteristics, adds a different dimension to our ML toolbox. Through practical examples, we saw these concepts in action, applying a BERT model for text classification and a Vision Transformer for image classification.
But, as we’ve come to appreciate, TL and pre-trained models, while powerful, are not the solution to all data...
