Archive for July 2019

Scaling technology and business processes are not equal. Since the beginning of the enterprise technology, scaling software has been a difficult task to get right inside large organisations. When it comes to Artificial Intelligence and Machine Learning, it becomes vastly more complicated. 

In this episode I propose a framework - in five pillars - for the business side of artificial intelligence.

 

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In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work. 

The questions that Charles answers in the show are essentially two:

  1. Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?

  2. How can we dominate DNN in a theoretically principled way?

 

References 

 

 

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In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 

I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 

 

References

Videflow Github official repository
https://github.com/videoflow/videoflow

 

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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.

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Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.

His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms.
There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more.

Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount.

 

References

Twitter https://twitter.com/davekopec

GitHub https://github.com/davecom

classicproblems.com

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