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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)
November 13, 2019November 13, 2019

Good data in, great decisions out.

Good data in, great decisions out. AI

The data governance framework you need for your organization The adoption of artificial intelligence is rapidly spreading across many businesses. […]

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August 21, 2019November 20, 2019

Effective community detection with Markov Clustering

Effective community detection with Markov Clustering AI

“If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised […]

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August 5, 2019November 16, 2019

Waterfall or Agile? The best methodology for AI and machine learning

Waterfall or Agile? The best methodology for AI and machine learning Blog

The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and […]

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July 22, 2019November 11, 2019

How businesses can scale Artificial Intelligence & Machine Learning

How businesses can scale Artificial Intelligence & Machine Learning AI

Scaling technology and business processes are not equal, here is a framework for the business side of artificial intelligence. Since […]

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May 9, 2019November 24, 2019

3 benefits of deep feature extraction and transfer learning

3 benefits of deep feature extraction and transfer learning AI

There’s no real news stating that Feature extraction [1] represents a fundamental step in any machine learning pipeline. Whenever a […]

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May 9, 2019November 11, 2019

No data, no honey: the grim reality of machine learning in medicine (and other domains)

No data, no honey: the grim reality of machine learning in medicine (and other domains) blockchain

The increasing amount of data collected by the numerous internet services people use every day has opened doors to a […]

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May 6, 2019November 20, 2019

Entropy in machine learning

Entropy in machine learning AI

It all starts from physics The entropy of an isolated system never decreases… Everyone at school, at some point of […]

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April 28, 2019May 3, 2019

The physics of optimization algorithms

The physics of optimization algorithms AI

In a previous post, we mentioned that one reason for the successes of deep learning methods in the last decade […]

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January 21, 2019March 25, 2019

Know what you predict: estimating uncertainty with neural networks

Know what you predict: estimating uncertainty with neural networks Blog

In most of the practical use cases, data scientists are satisfied by machine learning models that simply make predictions. Given […]

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December 20, 2018March 25, 2019

Episode 49: The promises of Artificial Intelligence

Episode 49: The promises of Artificial Intelligence Podcast

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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  • Good data in, great decisions out.
  • Effective community detection with Markov Clustering
  • Waterfall or Agile? The best methodology for AI and machine learning
  • How businesses can scale Artificial Intelligence & Machine Learning
  • Neural networks that don’t need to be trained

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