podcast

May 7, 2019

Episode 59: How to fool a smart camera with deep learning

In this episode I met three crazy researchers from KULeuven (Belgium) who found a method to fool surveillance cameras and stay hidden just by holding a special t-shirt. We discussed about the technique they used and some consequences of their findings. T...
April 30, 2019

Episode 58: There is physics in deep learning!

There is a connection between gradient descent based optimizers and the dynamics of damped harmonic oscillators. What does that mean? We now have a better theory for optimization algorithms.In this episode I explain how all this works. All the formulas I...
April 23, 2019

Episode 57: Neural networks with infinite layers

How are differential equations related to neural networks? What are the benefits of re-thinking neural network as a differential equation engine? In this episode we explain all this and we provide some material that is worth learning. Enjoy the show!   R...
April 16, 2019

Episode 56: The graph network

Since the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathematical symbols to represent objects and the relationship between them, in order to dep...
April 9, 2019

Episode 55: Beyond deep learning

The successes that deep learning systems have achieved in the last decade in all kinds of domains are unquestionable. Self-driving cars, skin cancer diagnostics, movie and song recommendations, language translation, automatic video surveillance, digital ...
March 9, 2019

Episode 54: Reproducible machine learning

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. Everyone will have good reasons to change parts of your...
January 23, 2019

Episode 53: Estimating uncertainty with neural networks

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 realistic cases in which Bayesian sampling is not really a...
January 17, 2019

Episode 52: why do machine learning models fail? [RB]

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 conditions a well trained machine learning model can gene...
Episode 51: Decentralized machine learning in the data marketplace (part 2)
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