Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on April 12th and 13th in Hamburg, Germany. You can read about one participant’s experience in my last workshop: Big Data – a buzz word you can find everywhere these days, from nerdy blogs to scientific research papers and even in the news. But how does Big Data Analysis work, exactly?
On Wednesday, April 25th 2018 I am going to talk about explainability of machine learning models at the Minds Mastering Machines conference in Cologne. The conference will be in German, though. ERKLÄRBARKEIT VON MACHINE LEARNING: WIE KÖNNEN WIR VERTRAUEN IN KOMPLEXE MODELLE SCHAFFEN? Mit Machine-Learning getroffene Entscheidungen sind inhärent schwierig – wenn nicht gar unmöglich – nachzuvollziehen. Die Komplexität einiger der besten Modelle, wie Neuronale Netzwerke, ist genau das, was sie so erfolgreich macht.
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.
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.
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.
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.
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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