Episode 62: AI and the future of banking with Chris Skinner

In this episode I have a wonderful conversation with Chris Skinner. Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”. After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regulation and technology in finance.

Episode 61: The 4 best use cases of entropy in machine learning

It all starts from physics. The entropy of an isolated system never decreases… Everyone at school, at some point of his life, learned this in his physics class. What does this have to do with machine learning? To find out, listen to the show.   References Entropy in machine learning https://amethix.com/entropy-in-machine-learning/

Episode 60: Predicting your mouse click (and a crash course in deeplearning)

Deep learning is the future. Get a crash course on deep learning. Now! In this episode I speak to Oliver Zeigermann, author of Deep Learning Crash Course published by Manning Publications at https://www.manning.com/livevideo/deep-learning-crash-course Oliver (Twitter: @DJCordhose) is a veteran of neural networks and machine learning. In addition to the course - that teaches you concepts from prototype to production - he's working on a really cool project that predicts something people do every day... clicking their mouse.  If you use promo code poddatascienceathome19 you get a 40% discount for all products on the Manning platform Enjoy the show!   References:   Deep Learning Crash Course (Manning Publications) https://www.manning.com/livevideo/deep-learning-crash-course?a_aid=djcordhose&a_bid=e8e77cbf   Companion notebooks for the code samples of the video course "Deep Learning Crash Course" https://github.com/DJCordhose/deep-learning-crash-course-notebooks/blob/master/README.md   Next-button-to-click predictor source code https://github.com/DJCordhose/ux-by-tfjs  

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. They published their paper on Arxiv and made their source code available at https://gitlab.com/EAVISE/adversarial-yolo Enjoy the show!   References Fooling automated surveillance cameras: adversarial patches to attack person detection Simen Thys, Wiebe Van Ranst, Toon Goedemé   Eavise Research Group KULeuven (Belgium)https://iiw.kuleuven.be/onderzoek/eavise