Data Science at Home

Technology, machine learning and algorithms

Episodes Date

In this episode I speak about how important reproducible machine learning pipelines are. When you are collaborating with diverse teams, several tasks will be distributed among different individuals. E...
March 9, 2019
Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by design. However, there are many real...
January 23, 2019
The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific con...
January 17, 2019
In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Pr...
January 8, 2019
In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress - one providing secure machine learning on isolated data, the other implementing a decentral...
December 26, 2018
It's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most misleading st...
December 19, 2018
In this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home office.There are several reasons ...
October 21, 2018
Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long lis...
September 11, 2018
In this episode I continue the conversation from the previous one, about failing machine learning models. When data scientists have access to the distributions of training and testing datasets it beco...
September 4, 2018
The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific con...
August 28, 2018

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