Data Science at Home
A podcast by Francesco Gadaleta
Categories:
254 Episodes
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Is Rust flexible enough for a flexible data model? (Ep. 137)
Published: 1/02/2021 -
Is Apple M1 good for machine learning? (Ep.136)
Published: 25/01/2021 -
Rust and deep learning with Daniel McKenna (Ep. 135)
Published: 18/01/2021 -
Scaling machine learning with clusters and GPUs (Ep. 134)
Published: 31/12/2020 -
What is data ethics? (Ep. 133)
Published: 19/12/2020 -
A Standard for the Python Array API (Ep. 132)
Published: 8/12/2020 -
What happens to data transfer after Schrems II? (Ep. 131)
Published: 4/12/2020 -
Test-First Machine Learning [RB] (Ep. 130)
Published: 1/12/2020 -
Similarity in Machine Learning (Ep. 129)
Published: 24/11/2020 -
Distill data and train faster, better, cheaper (Ep. 128)
Published: 17/11/2020 -
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127)
Published: 11/11/2020 -
Top-3 ways to put machine learning models into production (Ep. 126)
Published: 7/11/2020 -
Remove noise from data with deep learning (Ep.125)
Published: 3/11/2020 -
What is contrastive learning and why it is so powerful? (Ep. 124)
Published: 30/10/2020 -
Neural search (Ep. 123)
Published: 23/10/2020 -
Let's talk about federated learning (Ep. 122)
Published: 18/10/2020 -
How to test machine learning in production (Ep. 121)
Published: 11/10/2020 -
Why synthetic data cannot boost machine learning (Ep. 120)
Published: 26/09/2020 -
Machine learning in production: best practices [LIVE from twitch.tv]
Published: 16/09/2020 -
Testing in machine learning: checking deeplearning models (Ep. 118)
Published: 4/09/2020
Artificial Intelligence, algorithms and tech tales that are shaping the world