The topic will be “Information Retrieval from Knowledge Graphs and non-Structured data sources”. The full schedule of the meeting is here: http://london.pydata.org/schedule/

My talk on Earl Conference, London 2015 will be “Building interactive visualizations with Shiny to explore data from social and health care in UK”. Social and Health care in UK produce annulay immense data aggregated at several levels. To add to the challenge, data is...

This is my newest visualization work using UK data. http://armandoanalytics.blogspot.pt/2015/03/health-social-care-uk.html

Sitting atop a mountainous treasure trove of data, most all businesses are thirsty for people who can take a massive set of data and turn it into something meaningful. Whether it’s pinpointing new sources of revenue or predicting the next, best product feature, businesses are...

There have been tremendous advances in AI and lots of buzz around the possibility of the singularity coming soon acclaimed by scientists like Kurzweil. Inspired by the success of Deep learning, the new kid in town that took the field by storm winning almost all machine learning...

Deep Learning is the buzz word of the moment. But can it revolutionize artificial intelligence as some suggests? There is good reason to be excited about deep learning, a sophisticated “machine learning” algorithm that far exceeds many of its predecessors in its abilities to...

“We have build a marvelous model using Deep Learning!”. Sure, but what does it means when heading for production?  Does the data is as clean at it should? Is it available? At what cost? What are legal and privacy implications? How will customers react? Will decisions...

I’m very thankful to Jurgen Schimdhuber for having cited my work on HLVQ on its excellent review as one of the predecessors of Deep Learning. It was published in 2003.

I’m very happy that any area of machine learning (aka, statistical inference and decision-making) is beginning to make impact on real-world problems. I’m also overall happy with the rebranding associated with the usage of the term “deep learning” instead...