In this episode I don't talk about data. In fact, I talk about metadata.
While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been proved how powerful is the signal carried by metadata, that is all data that is invisible to the end user.
Behind a tweet of 140 characters there are more than 140 fields of data that draw a much more detailed profile of the sender and the content she is producing... without ever considering the tweet itself.
References
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information https://www.ucl.ac.uk/~ucfamus/papers/icwsm18.pdf
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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