The Future of (Transfer Learning in) Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.
NLP Model Selection
NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.
Transfer Learning In NLP
A brief history of Transfer Learning In NLP
🦄 How to build a SOTA Conversational AI with Transfer Learning
Train a dialog agent leveraging transfer Learning from an OpenAI GPT and GPT-2 Transformer language model.
Learning to See before Learning to Act: Visual Pre-training
We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects.
BLEURT: Learning Robust Metrics for Text Generation
A metric for Natural Language Generation based on transfer learning.
Transfer Learning - Machine Learning's Next Frontier
This post gives an overview of transfer learning, motivates why it warrants our application, and discusses practical applications and methods.
The State of Transfer Learning in NLP
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. It highlights key insights and takeaways and provides updates based on recent ...
Scene Classification using Pytorch and Fast.ai
The objective is to classify Multi-label images using deep learning. Here I have used Fast.ai library for implementing the model.
Applying Transfer Learning using PyTorch C++ API (Dogs vs Cats)
Loading Custom Dataset in the PyTorch C++ API isn't straight forward. This blog helps you do that, and explains transfer learning implementation in C++.
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