Latest DeepMind research on computer vision and scene rendering

The latest DeepMind research paper on computer vision [1] and neural scene rendering appears to be ground breaking and a milestone for the field of computer vision. For anyone already acquainted with the application of deep neural networks for computer vision will know, the training process of those networks requires the input features of an … Continue reading Latest DeepMind research on computer vision and scene rendering

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Statistical and Machine Learning Models for Time Series Forecasting

Following a recent post in this blog about Time Series Analysis, today I return to the same topic. But now from a statical learning and machine learning perspectives. The video is also from PyData, the presenter is the same - Dr. Jeffrey Yau - but this time it was in New York City and it is … Continue reading Statistical and Machine Learning Models for Time Series Forecasting

ReBlog from The Morning Paper: DeepTest: automated testing of deep-neural-network-driven autonomous cars — the morning paper

DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE’18 How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under […] … Continue reading ReBlog from The Morning Paper: DeepTest: automated testing of deep-neural-network-driven autonomous cars — the morning paper

Thoughts on Machine Learning and Artificial Intelligence

Thoughts about Machine Learning(ML) and Artificial Intelligence(AI): is it right to say that in order for the frameworks to generalize better about the data that is their input, they must somehow try to go beyond the need for strict dimensionality reduction algorithms they currently employ? My bet is yes, and specifically within the context of … Continue reading Thoughts on Machine Learning and Artificial Intelligence

The Information is back: PyData Ann Arbor, Machine Learning, Crowdsourcing and Cartoons

  It is a new year: 2018. And The Intelligence of Information is back with some new posts. Hopefully with better quality and significance for all the interested audience: that past audience, the future and else. I want to improve the coverage of the topics already familiar with this blog: Data Science, Machine Learning and … Continue reading The Information is back: PyData Ann Arbor, Machine Learning, Crowdsourcing and Cartoons

Sequence to sequence learning with Convolutional Neural networks

A team of researchers from Facebook AI research released an interesting paper about sequence to sequence learning with convolutional neural networks (CNNs). CNNs has been mainly used in computer vision implementations, being a state-of-the-art stack for the  the researche and development in object recognition or image recognition. Less often have CNNs been implemented for machine … Continue reading Sequence to sequence learning with Convolutional Neural networks