In this podcast I get inspired by Paul Done's presentation about The Six Principles for Building Robust Yet Flexible Shared Data Applications, and show how powerful of a language Rust is while still maintaining the flexibility of less strict languages.
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This episode is supported by Chapman’s Schmid College of Science and Technology, where master's and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.
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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)
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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)
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