Transfer learning
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem
See:
# Resources
- https://en.wikipedia.org/wiki/Transfer_learning
- https://github.com/artix41/awesome-transfer-learning
- Transfer Learning - Machine Learning’s Next Frontier
- Domain adaptation
- Domain adaptation is a field associated with machine learning and transfer learning. This scenario arises when we aim at learning from a source data distribution a well performing model on a different (but related) target data distribution
- Fine-tuning with Keras and Deep Learning
# Courses
- #COURSE Transfer Learning | Kaggle
# Code
- #CODE
pytorch-adapt
- Domain adaptation made easy. Fully featured, modular, and customizable
- https://kevinmusgrave.github.io/pytorch-adapt/
- #CODE
TLlib
- open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API
- #code Salad
- #code Robustness