Data Science at Home 

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Title	Date published
Compressing deep learning models: rewinding (Ep.105)	2020-06-01
Compressing deep learning models: distillation (Ep.104)	2020-05-20
Pandemics and the risks of collecting data (Ep. 103)	2020-05-08
Why average can get your predictions very wrong (ep. 102)	2020-04-19
Activate deep learning neurons faster with Dynamic RELU (ep. 101)	2020-04-01
WARNING!! Neural networks can memorize secrets (ep. 100)	2020-03-23
Attacks to machine learning model: inferring ownership of training data (Ep. 99)	2020-03-14
Don't be naive with data anonymization (Ep. 98)	2020-03-08
Why sharing real data is dangerous (Ep. 97)	2020-03-01
Building reproducible machine learning in production (Ep. 96)	2020-02-22
Bridging the gap between data science and data engineering: metrics (Ep. 95)	2020-02-14
A big welcome to Pryml: faster machine learning applications to production (Ep. 94)	2020-02-07
It's cold outside. Let's speak about AI winter (Ep. 93)	2019-12-31
The dark side of AI: bias in the machine (Ep. 92)	2019-12-28
The dark side of AI: metadata and the death of privacy (Ep. 91)	2019-12-23
The dark side of AI: recommend and manipulate (Ep. 90)	2019-12-11
The dark side of AI: social media and the optimization of addiction (Ep. 89)	2019-12-03
More powerful deep learning with transformers (Ep. 84) (Rebroadcast)	2019-11-27
How to improve the stability of training a GAN (Ep. 88)	2019-11-18
What if I train a neural network with random data? (with Stanisław Jastrzębski) (Ep. 87)	2019-11-12

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