How can you detect and address concept drift in fine-tuned models deployed in real-world applications?

Dec 15, 2023 ... Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific ... Modifying the "initial_epoch" arg when doing fine tuning and transfer learning. ... mrdbourke / tensorflow-deep-learning Public. Notifications You must be signed ... Apr 15, 2020 ... The most common incarnation of transfer learning in the context of deep learning is the following workflow: Take layers from a previously ... Feb 13, 2025 ... Fine-tuning is a form of transfer learning where the pre-trained model is adjusted to better fit the new data. There are several steps involved ... What is fine-tuning in machine learning and AI? ... Fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, ... ... Fine-tuning, distillation, and prompt engineering in the Machine Learning Crash Course. How fine-tuning works. The goal of fine-tuning is to further improve ... Aug 16, 2024 ... The very last classification layer (on "top", as most diagrams of machine learning models go from bottom to top) is not very useful. Instead ... Oct 8, 2024 ... Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery. Jul 17, 2023 ... As a machine learning practitioner, you might often find yourself in a situation where you're fine-tuning a pre-trained model for your specific ... May 2, 2022 ... An Indonesian recipe generator Deep Learning model trained by fine-tuning pre-trained models such as T5, BART, and GPT-2