Pytorch Transfer Learning

[Flyheap]

Key Objectives:

Pre-trained Models: We aim to provide a comprehensive collection of pre-trained models in PyTorch, spanning different domains like computer vision, natural language processing, and more. These models should be well-documented and readily accessible to users.

Fine-Tuning Capabilities: PyTorch's ability to fine-tune pre-trained models for specific tasks is a powerful feature. We will work on making this process more user-friendly and efficient, enabling users to adapt models to their unique requirements.

Domain Transfer: Transfer learning extends beyond image classification. We will explore how PyTorch can be applied to various domains, including text analysis, audio processing, and reinforcement learning, with an emphasis on making these techniques accessible and practical.

Integration with Ecosystem: We recognize that PyTorch is often part of a broader ecosystem of tools and libraries. We will work on enhancing PyTorch's compatibility and integration with other popular deep learning and machine learning tools.

Best Practices: Transfer learning can be nuanced, and there are different strategies for different scenarios. We will aim to provide guidelines and best practices for effectively applying transfer learning in PyTorch.

[Lakshya-GG]

can you assign me this issue @Flyheap

[Flyheap]

ISSUE ASSIGNED @Lakshya-GG