Join me on an enlightening journey through the world of prompt engineering. Explore the multifaceted skills and strategies involved in harnessing the potential of large language models for various applications. From enhancing safety measures to augmenting models with domain knowledge, learn how prompt engineering is shaping the future of AI.
References
Rust and deep learning with Daniel McKenna (Ep. 135)
Scaling machine learning with clusters and GPUs (Ep. 134)
What is data ethics? (Ep. 133)
A Standard for the Python Array API (Ep. 132)
What happens to data transfer after Schrems II? (Ep. 131)
Test-First Machine Learning [RB] (Ep. 130)
Similarity in Machine Learning (Ep. 129)
Distill data and train faster, better, cheaper (Ep. 128)
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127)
Top-3 ways to put machine learning models into production (Ep. 126)
Remove noise from data with deep learning (Ep.125)
What is contrastive learning and why it is so powerful? (Ep. 124)
Neural search (Ep. 123)
Let's talk about federated learning (Ep. 122)
How to test machine learning in production (Ep. 121)
Why synthetic data cannot boost machine learning (Ep. 120)
Machine learning in production: best practices [LIVE from twitch.tv] (Ep. 119)
Testing in machine learning: checking deeplearning models (Ep. 118)
Testing in machine learning: generating tests and data (Ep. 117)
Why you care about homomorphic encryption (Ep. 116)
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