Last week I published a blog post about how easy it is to train image classification models with Keras. What I did not show in that post was how to use the model for making predictions. This, I will do here. But predictions alone are boring, so I’m adding explanations for the predictions using the lime package. I have already written a few blog posts (here, here and here) about LIME and have given talks (here and here) about it, too.

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Practical Deep Learning with Rachel Thomas: Sketchnotes from TWiMLAI talk: Practical Deep Learning with Rachel Thomas You can listen to the podcast here. In this episode, i’m joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course.

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I’ve been using keras and TensorFlow for a while now - and love its simplicity and straight-forward way to modeling. As part of the latest update to my Workshop about deep learning with R and keras I’ve added a new example analysis: Building an image classifier to differentiate different types of fruits And I was (again) suprised how fast and easy it was to build the model; it took not even half an hour and only around 100 lines of code (counting only the main code; for this post I added comments and line breaks to make it easier to read)!

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On May 21st and 22nd, I had the honor of having been chosen to attend the rOpenSci unconference 2018 in Seattle. It was a great event and I got to meet many amazing people! rOpenSci rOpenSci is a non-profit organisation that maintains a number of widely used R packages and is very active in promoting a community spirit around the R-world. Their core values are to have open and reproducible research, shared data and easy-to-use tools and to make all this accessible to a large number of people.

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Registration is now open for my 1.5-day workshop on deep learning with Keras and TensorFlow using R. It will take place on July 5th & 6th in Münster, 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?

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These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang: Sketchnotes from TWiMLAI talk: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang You can listen to the podcast here. In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies.

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When looking through the CRAN list of packages, I stumbled upon this little gem: pkgnet is an R library designed for the analysis of R libraries! The goal of the package is to build a graph representation of a package and its dependencies. And I thought it would be fun to play around with it. The little analysis I ended up doing was to compare dependencies of popular machine learning packages.

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