Published Jun 15, 2018

Transfer Learning

Explore the transformative power of transfer learning and its applications with Kyle Polich, as he delves into the future of NLP through automated feature engineering, and highlights the democratizing impact of open-source contributions by Sebastian Ruder, fostering AI innovation and collaboration.

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Episode Highlights

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Dexa/Kyle Polich

Transfer Learning

Topics covered

Popular Clips

Future Directions

Progress and Challenges

Open Source Code

Cross Lingual Learning

Exciting AI Developments

Unlocking Underresourced Languages

NLP's ImageNet Moment

Rigorous Techniques in Data Science

Transfer Learning Considerations

Transfer Learning and Active Learning

NLP Breakthroughs

Episode Highlights

Future Directions in NLP

Open Source Contributions

Transfer Learning Applications

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Serverless NLP Model Training

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Interpretable One Shot Learning

SpanBERT

ELMo

Named Entity Recognition

ML Ops Best Practices

AI Platforms

[MINI] Feed Forward Neural Networks

I LLM and You Can Too

word2vec

Understanding Neural Networks

Cross-lingual Short-text Matching

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Topics covered

Popular Clips

Episode Highlights

Future Directions in NLPThe discussion shifts to the theoretical underpinnings of rigorous techniques in data science and the progression of NLP. Kyle Polich and Sebastian Ruder explore the future of automated feature engineering and the challenges in achieving human-like language understanding.

Future Directions in NLP

Open Source ContributionsThe discussion shifts to the availability and impact of open-source resources in transfer learning, highlighting contributions by Sebastian Ruder. His work exemplifies how open-sourcing can democratize access to advanced AI tools, fostering innovation and collaboration.

Open Source Contributions

Transfer Learning ApplicationsKyle Polich explores the transformative potential of transfer learning in machine learning. He discusses how this technique allows models to leverage pre-trained knowledge for new tasks, especially in fields like computer vision and NLP.

Transfer Learning Applications

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