Recent Posts
Activate deep learning neurons faster with Dynamic RELU (ep. 101)
In this episode I briefly explain the concept behind activation functions in deep learning. One of the most widely used activation function is the rectified linear unit (ReLU). While there are several flavors of ReLU in the literature, in this episode I ...
WARNING!! Neural networks can memorize secrets (ep. 100)
One of the best features of neural networks and machine learning models is to memorize patterns from training data and apply those to unseen observations. That's where the magic is. However, there are scenarios in which the same machine learning models l...
Attacks to machine learning model: inferring ownership of training data (Ep. 99)
In this episode I explain a very effective technique that allows one to infer the membership of any record at hand to the (private) training dataset used to train the target model. The effectiveness of such technique is due to the fact that it works on b...
Don’t be naive with data anonymization (Ep. 98)
Masking, obfuscating, stripping, shuffling. All the above techniques try to do one simple thing: keeping the data private while sharing it with third parties. Unfortunately, they are not the silver bullet to confidentiality. All the players in the synthe...
Why sharing real data is dangerous (Ep. 97)
There are very good reasons why a financial institution should never share their data. Actually, they should never even move their data. Ever.In this episode I explain you why.
Building reproducible machine learning in production (Ep. 96)
Building reproducible models is essential for all those scenarios in which the lead developer is collaborating with other team members. Reproducibility in machine learning shall not be an art, rather it should be achieved via a methodical approach. In th...
Bridging the gap between data science and data engineering: metrics (Ep. 95)
Data science and data engineering are usually two different departments in organisations. Bridging the gap between the two is essential to success. Many times the brilliant applications created by data scientists don't find a match in production, just be...
A big welcome to Pryml: faster machine learning applications to production
Why so much silence? Building a company! That's why :) I am building pryml, a platform that allows data scientists build their applications on data they cannot get access to. This is the first of a series of episodes in which I will speak about the techn...
It’s cold outside. Let’s speak about AI winter (Ep. 93)
In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot o...
The dark side of AI: bias in the machine (Ep. 92)
This is the fourth and last episode of mini series "The dark side of AI". I am your host Francesco and I’m with Chiara Tonini from London. The title of today’s episode is Bias in the machine C: Francesco, today we are starting with an infuriating ...
