Artificial Intelligence and data governance
Author: Francesco Gadaleta
I am Chief Data Scientist and Director at Amethix Technologies and Host of podcast Data Science at Home My interests range from applied math, machine learning, cryptography and blockchain technology. I have worked in domains such as healthcare and pharmaceuticals, R&D in bioinformatics, and personal finance.Specialties
• Applied mathematics and statistics
• Programming in multiple languages from proof-of-concept to production
• Advanced Machine Learning and Artificial IntelligenceI have a weakness for remote working, highly-skilled individuals, sports and startups (in no particular order)
Effective community detection with Markov Clustering
“If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised […]
Waterfall or Agile? The best methodology for AI and machine learning
The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and […]
How businesses can scale Artificial Intelligence & Machine Learning
Scaling technology and business processes are not equal, here is a framework for the business side of artificial intelligence. Since […]
3 benefits of deep feature extraction and transfer learning
There’s no real news stating that Feature extraction [1] represents a fundamental step in any machine learning pipeline. Whenever a […]
No data, no honey: the grim reality of machine learning in medicine (and other domains)
The increasing amount of data collected by the numerous internet services people use every day has opened doors to a […]
Entropy in machine learning
It all starts from physics The entropy of an isolated system never decreases… Everyone at school, at some point of […]
The physics of optimization algorithms
In a previous post, we mentioned that one reason for the successes of deep learning methods in the last decade […]
Know what you predict: estimating uncertainty with neural networks
In most of the practical use cases, data scientists are satisfied by machine learning models that simply make predictions. Given […]
Episode 49: The promises of Artificial Intelligence
It’s always good to put in perspective all the findings in AI, in order to clear some of the most […]
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