Collection of some freely available talks in which I have been involved as a speaker for conferences, events and podcasts.
Datacast Interview: Building Data Science Projects
Adventures in Machine Learning: Data Paradoxes in Data Sets
Data Driven People Interview (Italian)
Microsoft Reactor of London: Paradoxes in Data Science
Paradoxes are a class of phenomena that arise when, although starting from premises known as true, we derive some sort of logically unreasonable result. As Machine Learning models create knowledge from data, this makes them susceptible to possible cognitive paradoxes between training and testing. In this talk, I walk you through some of the main paradoxes associated with Data Science and how they can be identified. This talk has also been performed for the Data Talks Club and Machine Learning Milan communities.
2020 SAS UK&I Forum
Intro to Viya demo session at the 2020 SAS UK&I Virtual Forum. More information about this event is available at this link.
Global AI On Tour: Causal Reasoning In Machine Learning
Nowadays Machine Learning models, are able to learn from data by identifying patterns in large datasets. Although, humans might be able to perform a same task after just examining a few examples. This is possible thanks to the inherit humans ability to understand causal relationships and use inductive inference in order to assimilate new information about the world. In this demonstration given at the Global AI On Tour conference, we are going to find out more about how to embed Causal Reasoning in Machine Learning.
DataScienceSeed: Causal Reasoning in ML: Spiegare “perché” (Italian)
Al giorno d’oggi le tecnologie di Machine Learning si basano solo sulle correlazioni tra le diverse “features”. Ció nonostante, questo approccio può eventualmente portare a conclusioni errate poiché correlazioni non implicano necessariamente causalità.
Towards Data Science: GPU Accelerated Data Analytics & Machine Learning
GPU acceleration is nowadays becoming more and more important. The main two drivers for this shift are:
- The world’s amount of data is doubling every year.
- Moore’s law is now coming to an end because of limitations imposed by the quantum realm.
As a demonstration for this shift, an increasing number of online data science platforms is now adding GPU enabled solutions. Some examples are: Kaggle, Google Colaboratory, Microsoft Azure and Amazon Web Services (AWS).
Google Developers Group (Zurich): Paradoxes in Data Science
GCP deployment instructions provided during the presentation are available at this link.