For those of you out there who speak German: I was interviewed for a tech podcast where I talked about machine learning, neural nets, why I love R and Rstudio and how I became a Data Scientist. You can download and listen to the podcast here: https://mies.me/2018/01/31/hmww17-machine-learning-mit-dr-shirin-glander/ In der aktuellen Episode gibt Dr. Shirin Glander (Twitter, Homepage) uns ein paar Einblicke in das Thema Machine Learning. Wir klären zunächst, was Machine Learning ist und welche Möglichkeiten es bietet bevor wir etwas mehr in die Tiefe gehen.

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I am happy to announce that on Tuesday, April 24th 2018 Uwe Friedrichsen and I will give a talk about Deep Learning - a Primer at the JAX conference in Mainz, Germany. Deep Learning is one of the “hot” topics in the AI area – a lot of hype, a lot of inflated expectation, but also quite some impressive success stories. As some AI experts already predict that Deep Learning will become “Software 2.

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley: Sketchnotes from TWiMLAI talk #94: Neuroevolution: Evolving Novel Neural Network Architectures with Kenneth Stanley You can listen to the podcast here. Kenneth studied under TWiML Talk #47 guest Risto Miikkulainen at UT Austin, and joined Uber AI Labs after Geometric Intelligence , the company he co-founded with Gary Marcus and others, was acquired in late 2016.

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In our next MünsteR R-user group meetup on March 5th, 2018 Frank Rühle will talk about bioinformatics and how to analyse genome data. You can RSVP here: http://meetu.ps/e/DDY1B/w54bW/f Next-Generation sequencing and array-based technologies provided a plethora of gene expression data in the public genomics databases. But how to get meaningful information and functional implications out of this vast amount of data? Various R-packages have been published by the Bioconductor user community for distinct kinds of analysis strategies.

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Learning State Representations with Yael Niv: https://twimlai.com/twiml-talk-92-learning-state-representations-yael-niv/ Sketchnotes from TWiMLAI talk #92: Learning State Representations with Yael Niv You can listen to the podcast here. In this interview Yael and I explore the relationship between neuroscience and machine learning. In particular, we discusses the importance of state representations in human learning, some of her experimental results in this area, and how a better understanding of representation learning can lead to insights into machine learning problems such as reinforcement and transfer learning.

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The plumber package for R makes it easy to expose existing R code as a webservice via an API (https://www.rplumber.io/, Trestle Technology, LLC 2017). You take an existing R script and make it accessible with plumber by simply adding a few lines of comments. If you have worked with Roxygen before, e.g. when building a package, you will already be familiar with the core concepts. If not, here are the most important things to know:

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Philosophy of Intelligence with Matthew Crosby: https://twimlai.com/twiml-talk-92-learning-state-representations-yael-niv/ Sketchnotes from TWiMLAI talk #92: Philosophy of Intelligence with Matthew Crosby You can listen to the podcast here. This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.

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