Transfer Learning and Applications

Overview of machine learning and deep learning, neural networks and optimization algorithms, Introduction to Transfer Learning and pretrained models, evaluation and model selection, Feature extraction using pretrained models, Techniques for fine-tuning pretrained models, Unsupervised domain adaptation and self-supervised learning, multi-task learning and applications, Big Transforms, Vision Transforms, Transfer learning in computer vision, Challenges and considerations in transfer learning for computer vision tasks, Transfer Learning in Natural Language Processing (NLP), Fine-tuning pretrained language models for various NLP tasks, Applications of transfer learning in sentiment analysis, text generation, and question answering, Meta-learning and few-shot learning, Lifelong learning.